#!/usr/bin/env python3
"""
HomeHarvest API Wrapper for CFR Command Center
Returns real property data from Realtor.com
"""

import json
import sys
import math
from homeharvest import scrape_property
import pandas as pd

def clean_value(val, default=0):
    """Clean NaN/NA/None values from pandas"""
    if val is None:
        return default
    if isinstance(val, float) and math.isnan(val):
        return default
    if pd.isna(val):
        return default
    return val

def search_comps(address: str, radius: float = 5.0, past_days: int = 90, limit: int = 20):
    """Search for comparable sold properties near an address"""
    try:
        properties = scrape_property(
            location=address,
            listing_type="sold",
            past_days=past_days,
            radius=radius,
            limit=limit
        )

        # Convert to simplified comp format
        comps = []
        for _, row in properties.iterrows():
            comp = {
                "address": clean_value(row.get("formatted_address"), clean_value(row.get("full_street_line"), "")),
                "city": clean_value(row.get("city"), ""),
                "state": clean_value(row.get("state"), ""),
                "zip": clean_value(row.get("zip_code"), ""),
                "beds": clean_value(row.get("beds"), 0),
                "baths": clean_value(row.get("full_baths"), 0) + clean_value(row.get("half_baths"), 0) * 0.5,
                "sqft": clean_value(row.get("sqft"), 0),
                "soldPrice": clean_value(row.get("sold_price"), clean_value(row.get("last_sold_price"), 0)),
                "soldDate": str(clean_value(row.get("last_sold_date"), ""))[:10] if clean_value(row.get("last_sold_date"), "") else "",
                "pricePerSqft": clean_value(row.get("price_per_sqft"), 0),
                "yearBuilt": clean_value(row.get("year_built"), 0),
                "propertyType": clean_value(row.get("style"), "SINGLE_FAMILY"),
                "lotSize": clean_value(row.get("lot_sqft"), 0),
                "url": clean_value(row.get("property_url"), ""),
                "photos": clean_value(row.get("primary_photo"), ""),
            }
            comps.append(comp)

        return {"success": True, "comps": comps, "count": len(comps)}
    except Exception as e:
        return {"success": False, "error": str(e)}

def search_listings(location: str, listing_type: str = "for_sale", **filters):
    """Search for active listings or rentals"""
    try:
        properties = scrape_property(
            location=location,
            listing_type=listing_type,
            **filters
        )

        listings = []
        for _, row in properties.iterrows():
            listing = {
                "address": row.get("formatted_address", ""),
                "city": row.get("city", ""),
                "state": row.get("state", ""),
                "zip": row.get("zip_code", ""),
                "beds": row.get("beds", 0) or 0,
                "baths": (row.get("full_baths", 0) or 0) + (row.get("half_baths", 0) or 0) * 0.5,
                "sqft": row.get("sqft", 0) or 0,
                "listPrice": row.get("list_price", 0) or 0,
                "pricePerSqft": row.get("price_per_sqft", 0) or 0,
                "yearBuilt": row.get("year_built", 0) or 0,
                "propertyType": row.get("style", ""),
                "daysOnMarket": row.get("days_on_mls", 0) or 0,
                "url": row.get("property_url", ""),
                "photos": row.get("primary_photo", ""),
            }
            listings.append(listing)

        return {"success": True, "listings": listings, "count": len(listings)}
    except Exception as e:
        return {"success": False, "error": str(e)}

if __name__ == "__main__":
    command = sys.argv[1] if len(sys.argv) > 1 else "comps"

    if command == "comps":
        address = sys.argv[2] if len(sys.argv) > 2 else "Lakeland, FL"
        radius = float(sys.argv[3]) if len(sys.argv) > 3 else 5.0
        result = search_comps(address, radius)
    elif command == "listings":
        location = sys.argv[2] if len(sys.argv) > 2 else "Lakeland, FL"
        listing_type = sys.argv[3] if len(sys.argv) > 3 else "for_sale"
        result = search_listings(location, listing_type)
    else:
        result = {"error": "Unknown command. Use: comps <address> [radius] or listings <location> [type]"}

    print(json.dumps(result, indent=2))


Now I need to update the Python script to:
1. Accept property_type as a parameter2. Use 150 days by default instead of 903. Filter results by property type

Then update the Next.js comps search route to pass the property type from the request.

edit

def search_comps(address: str, radius: float = 5.0, past_days: int = 90, limit: int = 20):
    """Search for comparable sold properties near an address"""
    try:
        properties = scrape_property(
            location=address,
            listing_type="sold",
            past_days=past_days,
            radius=radius,
            limit=limit
        )

        # Convert to simplified comp format
        comps = []
        for _, row in properties.iterrows():
            comp = {
                "address": clean_value(row.get("formatted_address"), clean_value(row.get("full_street_line"), "")),
                "city": clean_value(row.get("city"), ""),
                "state": clean_value(row.get("state"), ""),
                "zip": clean_value(row.get("zip_code"), ""),
                "beds": clean_value(row.get("beds"), 0),
                "baths": clean_value(row.get("full_baths"), 0) + clean_value(row.get("half_baths"), 0) * 0.5,
                "sqft": clean_value(row.get("sqft"), 0),
                "soldPrice": clean_value(row.get("sold_price"), clean_value(row.get("last_sold_price"), 0)),
                "soldDate": str(clean_value(row.get("last_sold_date"), ""))[:10] if clean_value(row.get("last_sold_date"), "") else "",
                "pricePerSqft": clean_value(row.get("price_per_sqft"), 0),
                "yearBuilt": clean_value(row.get("year_built"), 0),
                "propertyType": clean_value(row.get("style"), "SINGLE_FAMILY"),
                "lotSize": clean_value(row.get("lot_sqft"), 0),
                "url": clean_value(row.get("property_url"), ""),
                "photos": clean_value(row.get("primary_photo"), ""),
            }
            comps.append(comp)

        return {"success": True, "comps": comps, "count": len(comps)}

def search_comps(address: str, radius: float = 5.0, past_days: int = 150, limit: int = 30, property_type: str = None):
    """Search for comparable sold properties near an address

    Args:
        address: Location to search
        radius: Search radius in miles
        past_days: Days to look back (default 150 = ~5 months)
        limit: Max results
        property_type: Filter by type (mobile, single_family, multi_family, etc.)
    """
    try:
        # Map property type names to HomeHarvest types
        type_mapping = {
            "mobile home": "mobile",
            "mobile": "mobile",
            "manufactured": "mobile",
            "single family": "single_family",
            "single_family": "single_family",
            "multi family": "multi_family",
            "multi_family": "multi_family",
            "condo": "condos",
            "townhouse": "townhomes",
        }

        # Build property_type filter for HomeHarvest
        hh_property_type = None
        if property_type:
            hh_property_type = type_mapping.get(property_type.lower())

        # Scrape properties
        scrape_args = {
            "location": address,
            "listing_type": "sold",
            "past_days": past_days,
            "radius": radius,
            "limit": limit,
        }

        if hh_property_type:
            scrape_args["property_type"] = [hh_property_type]

        properties = scrape_property(**scrape_args)

        # Convert to simplified comp format
        comps = []
        for _, row in properties.iterrows():
            row_type = clean_value(row.get("style"), "SINGLE_FAMILY")

            # Additional client-side filtering if property_type was specified
            if property_type and hh_property_type:
                # Skip if type doesn't match (HomeHarvest filter isn't always perfect)
                if hh_property_type == "mobile" and "MOBILE" not in str(row_type).upper():
                    continue

            comp = {
                "address": clean_value(row.get("formatted_address"), clean_value(row.get("full_street_line"), "")),
                "city": clean_value(row.get("city"), ""),
                "state": clean_value(row.get("state"), ""),
                "zip": clean_value(row.get("zip_code"), ""),
                "beds": clean_value(row.get("beds"), 0),
                "baths": clean_value(row.get("full_baths"), 0) + clean_value(row.get("half_baths"), 0) * 0.5,
                "sqft": clean_value(row.get("sqft"), 0),
                "soldPrice": clean_value(row.get("sold_price"), clean_value(row.get("last_sold_price"), 0)),
                "soldDate": str(clean_value(row.get("last_sold_date"), ""))[:10] if clean_value(row.get("last_sold_date"), "") else "",
                "pricePerSqft": clean_value(row.get("price_per_sqft"), 0),
                "yearBuilt": clean_value(row.get("year_built"), 0),
                "propertyType": row_type,
                "lotSize": clean_value(row.get("lot_sqft"), 0),
                "url": clean_value(row.get("property_url"), ""),
                "photos": clean_value(row.get("primary_photo"), ""),
            }
            comps.append(comp)

        return {"success": True, "comps": comps, "count": len(comps), "propertyTypeFilter": property_type}


Successfully replaced text in /home/crogers2287/cfr-command-center/scripts/homeharvest_api.py.

     ...
  19     if pd.isna(val):
  20         return default
  21     return val
  22
- 23 def search_comps(address: str, radius: float = 5.0, past_days: int = 90, limit: int = 20):
- 24     """Search for comparable sold properties near an address"""
+ 23 def search_comps(address: str, radius: float = 5.0, past_days: int = 150, limit: int = 30, property_type: str = None):
+ 24     """Search for comparable sold properties near an address
+ 25
+ 26     Args:
+ 27         address: Location to search
+ 28         radius: Search radius in miles
+ 29         past_days: Days to look back (default 150 = ~5 months)
+ 30         limit: Max results
+ 31         property_type: Filter by type (mobile, single_family, multi_family, etc.)
+ 32     """
  25     try:
- 26         properties = scrape_property(
- 27             location=address,
- 28             listing_type="sold",
- 29             past_days=past_days,
- 30             radius=radius,
- 31             limit=limit
- 32         )
+ 34         # Map property type names to HomeHarvest types
+ 35         type_mapping = {
+ 36             "mobile home": "mobile",
+ 37             "mobile": "mobile",
+ 38             "manufactured": "mobile",
+ 39             "single family": "single_family",
+ 40             "single_family": "single_family",
+ 41             "multi family": "multi_family",
+ 42             "multi_family": "multi_family",
+ 43             "condo": "condos",
+ 44             "townhouse": "townhomes",
+ 45         }
  33
+ 47         # Build property_type filter for HomeHarvest
+ 48         hh_property_type = None
+ 49         if property_type:
+ 50             hh_property_type = type_mapping.get(property_type.lower())
+ 51
+ 52         # Scrape properties
+ 53         scrape_args = {
+ 54             "location": address,
+ 55             "listing_type": "sold",
+ 56             "past_days": past_days,
+ 57             "radius": radius,
+ 58             "limit": limit,
+ 59         }
+ 60
+ 61         if hh_property_type:
+ 62             scrape_args["property_type"] = [hh_property_type]
+ 63
+ 64         properties = scrape_property(**scrape_args)
+ 65
  34         # Convert to simplified comp format
  35         comps = []
  36         for _, row in properties.iterrows():
+ 69             row_type = clean_value(row.get("style"), "SINGLE_FAMILY")
+ 70
+ 71             # Additional client-side filtering if property_type was specified
+ 72             if property_type and hh_property_type:
+ 73                 # Skip if type doesn't match (HomeHarvest filter isn't always perfect)
+ 74                 if hh_property_type == "mobile" and "MOBILE" not in str(row_type).upper():
+ 75                     continue
+ 76
  37             comp = {
  38                 "address": clean_value(row.get("formatted_address"), clean_value(row.get("full_street_line"), "")),
  39                 "city": clean_value(row.get("city"), ""),
  40                 "state": clean_value(row.get("state"), ""),
  41                 "zip": clean_value(row.get("zip_code"), ""),
  42                 "beds": clean_value(row.get("beds"), 0),
  43                 "baths": clean_value(row.get("full_baths"), 0) + clean_value(row.get("half_baths"), 0) * 0.5,
  44                 "sqft": clean_value(row.get("sqft"), 0),
  45                 "soldPrice": clean_value(row.get("sold_price"), clean_value(row.get("last_sold_price"), 0)),
  46                 "soldDate": str(clean_value(row.get("last_sold_date"), ""))[:10] if clean_value(row.get("last_sold_date"), "") else "",
  47                 "pricePerSqft": clean_value(row.get("price_per_sqft"), 0),
  48                 "yearBuilt": clean_value(row.get("year_built"), 0),
- 49                 "propertyType": clean_value(row.get("style"), "SINGLE_FAMILY"),
+ 89                 "propertyType": row_type,
  50                 "lotSize": clean_value(row.get("lot_sqft"), 0),
  51                 "url": clean_value(row.get("property_url"), ""),
  52                 "photos": clean_value(row.get("primary_photo"), ""),
  53             }
  54             comps.append(comp)
  55
- 56         return {"success": True, "comps": comps, "count": len(comps)}
+ 96         return {"success": True, "comps": comps, "count": len(comps), "propertyTypeFilter": property_type}
  57     except Exception as e:
  58         return {"success": False, "error": str(e)}
  59
  60 def search_listings(location: str, listing_type: str = "for_sale", **filters):
     ...


Now I need to update the command line interface at the bottom of the script to accept property_type.

if __name__ == "__main__":
    command = sys.argv[1] if len(sys.argv) > 1 else "comps"

    if command == "comps":
        address = sys.argv[2] if len(sys.argv) > 2 else "Lakeland, FL"
        radius = float(sys.argv[3]) if len(sys.argv) > 3 else 5.0
        result = search_comps(address, radius)

if __name__ == "__main__":
    command = sys.argv[1] if len(sys.argv) > 1 else "comps"

    if command == "comps":
        address = sys.argv[2] if len(sys.argv) > 2 else "Lakeland, FL"
        radius = float(sys.argv[3]) if len(sys.argv) > 3 else 5.0
        property_type = sys.argv[4] if len(sys.argv) > 4 else None
        result = search_comps(address, radius, property_type=property_type)


     ...
 135
 136     if command == "comps":
 137         address = sys.argv[2] if len(sys.argv) > 2 else "Lakeland, FL"
 138         radius = float(sys.argv[3]) if len(sys.argv) > 3 else 5.0
-139         result = search_comps(address, radius)
+139         property_type = sys.argv[4] if len(sys.argv) > 4 else None
+140         result = search_comps(address, radius, property_type=property_type)
 140     elif command == "listings":
 141         location = sys.argv[2] if len(sys.argv) > 2 else "Lakeland, FL"
 142         listing_type = sys.argv[3] if len(sys.argv) > 3 else "for_sale"
 143         result = search_listings(location, listing_type)
     ...


Now I need to update the Next.js comps search route to:1. Accept propertyType in the request body
2. Pass it to the Python script

Now update the API route to pass property type:


import { NextRequest, NextResponse } from "next/server";
import { exec } from "child_process";
import { promisify } from "util";
import { suggestSearchQueries } from "@/lib/gemini";
import type { Comp, CompSummary } from "@/types/comp";

const execAsync = promisify(exec);

// Call Python HomeHarvest script for REAL data
async function getRealComps(address: string, radius: number = 2): Promise<Comp[]> {
  const scriptPath = "/home/crogers2287/cfr-command-center/scripts/homeharvest_api.py";
  const venvPython = "/home/crogers2287/cfr-command-center/venv/bin/python3";

  try {
    const { stdout } = await execAsync(
      `${venvPython} ${scriptPath} comps "${address}" ${radius}`
    );

    const result = JSON.parse(stdout);

    if (!result.success) {
      console.error("HomeHarvest error:", result.error);
      return [];
    }

    // Transform HomeHarvest format to our Comp format
    return result.comps.map((c: Record<string, unknown>, i: number) => ({
      id: `comp-${i}`,
      address: c.address as string,
      city: c.city as string,
      state: c.state as string,
      zip: c.zip as string,
      propertyType: (c.propertyType as string)?.replace(/_/g, " ") || "Single Family",
      beds: (c.beds as number) || 0,
      baths: (c.baths as number) || 0,
      sqft: (c.sqft as number) || 0,
      yearBuilt: (c.yearBuilt as number) || 0,
      soldPrice: (c.soldPrice as number) || 0,
      pricePerSqft: (c.pricePerSqft as number) || 0,
      soldDate: c.soldDate as string,
      daysOnMarket: 30, // Not provided by HomeHarvest
      source: "Realtor.com",
      similarity: calculateSimilarity(c, radius),
      photos: c.photos as string,
      url: c.url as string,
    }));
  } catch (error) {
    console.error("Failed to get real comps:", error);
    return [];
  }
}

// Calculate similarity score based on data quality
function calculateSimilarity(comp: Record<string, unknown>, radius: number): number {
  let score = 70; // Base score

  // Bonus for having sqft data
  if (comp.sqft && (comp.sqft as number) > 0) score += 10;

  // Bonus for having year built

[100 more lines in file. Use offset=61 to continue.]


I need to update the getRealComps function to accept propertyType and pass it to the Python script.

// Call Python HomeHarvest script for REAL data
async function getRealComps(address: string, radius: number = 2): Promise<Comp[]> {
  const scriptPath = "/home/crogers2287/cfr-command-center/scripts/homeharvest_api.py";
  const venvPython = "/home/crogers2287/cfr-command-center/venv/bin/python3";

  try {
    const { stdout } = await execAsync(
      `${venvPython} ${scriptPath} comps "${address}" ${radius}`
    );

// Call Python HomeHarvest script for REAL data
async function getRealComps(address: string, radius: number = 2, propertyType?: string): Promise<Comp[]> {
  const scriptPath = "/home/crogers2287/cfr-command-center/scripts/homeharvest_api.py";
  const venvPython = "/home/crogers2287/cfr-command-center/venv/bin/python3";

  try {
    // Build command with optional property type filter
    const typeArg = propertyType ? `"${propertyType}"` : '""';
    const { stdout } = await execAsync(
      `${venvPython} ${scriptPath} comps "${address}" ${radius} ${typeArg}`
    );


Successfully replaced text in /home/crogers2287/cfr-command-center/src/app/api/comps/search/route.ts.

     ...
   6
   7 const execAsync = promisify(exec);
   8
   9 // Call Python HomeHarvest script for REAL data
- 10 async function getRealComps(address: string, radius: number = 2): Promise<Comp[]> {
+ 10 async function getRealComps(address: string, radius: number = 2, propertyType?: string): Promise<Comp[]> {
  11   const scriptPath = "/home/crogers2287/cfr-command-center/scripts/homeharvest_api.py";
  12   const venvPython = "/home/crogers2287/cfr-command-center/venv/bin/python3";
  13
  14   try {
+ 15     // Build command with optional property type filter
+ 16     const typeArg = propertyType ? `"${propertyType}"` : '""';
  15     const { stdout } = await execAsync(
- 16       `${venvPython} ${scriptPath} comps "${address}" ${radius}`
+ 18       `${venvPython} ${scriptPath} comps "${address}" ${radius} ${typeArg}`
  17     );
  18
  19     const result = JSON.parse(stdout);
  20
     ...


Now I need to update the POST handler to extract propertyType from the request and pass it to getRealComps.


      avgPricePerSqft: 0,
      avgDaysOnMarket: 0,
      medianPrice: 0,
      priceRange: { low: 0, high: 0 },
    };
  }

  const prices = comps.map((c) => c.soldPrice).sort((a, b) => a - b);
  const total = prices.reduce((sum, p) => sum + p, 0);

  // Filter out extreme outliers (mobile homes, land)
  const validComps = comps.filter(c =>
    c.soldPrice > 10000 &&
    c.propertyType !== "MOBILE" &&
    c.propertyType !== "LAND"
  );

  const validPrices = validComps.length > 0
    ? validComps.map(c => c.soldPrice).sort((a, b) => a - b)
    : prices;

  return {
    count: comps.length,
    avgSoldPrice: Math.round(total / comps.length),
    avgPricePerSqft: Math.round(
      comps.reduce((sum, c) => sum + (c.pricePerSqft || 0), 0) / comps.length
    ),
    avgDaysOnMarket: Math.round(
      comps.reduce((sum, c) => sum + (c.daysOnMarket || 30), 0) / comps.length
    ),
    medianPrice: validPrices[Math.floor(validPrices.length / 2)] || prices[0],
    priceRange: {
      low: validPrices[0] || prices[0],
      high: validPrices[validPrices.length - 1] || prices[prices.length - 1],
    },
  };
}

export async function POST(request: NextRequest) {
  try {
    const body = await request.json();
    const { address, beds, baths, sqft, radius = 2 } = body;

    if (!address) {
      return NextResponse.json({ error: "Address required" }, { status: 400 });
    }

    // Get AI suggestions for search terms
    let suggestions: string[] = [];
    try {
      suggestions = await suggestSearchQueries(address);
    } catch {
      // Continue without suggestions if AI fails
    }

    // Get REAL comps from HomeHarvest
    const comps = await getRealComps(address, radius);

    if (comps.length === 0) {
      return NextResponse.json(
        { error: "No comparable sales found in this area" },
        { status: 404 }
      );
    }

    const summary = calculateSummary(comps);

    return NextResponse.json({
      comps,
      summary,
      suggestions,
      source: "Realtor.com via HomeHarvest",
      realData: true,
    });
  } catch (error) {
    console.error("Comp search error:", error);
    return NextResponse.json(
      { error: "Failed to search comps" },
      { status: 500 }
    );
  }
}


I see a couple things to fix:
1. The calculateSummary function filters OUT mobile homes, but now we WANT mobile homes. I need to update that logic.
2. The POST handler needs to extract propertyType and pass it to getRealComps.

  // Filter out extreme outliers (mobile homes, land)
  const validComps = comps.filter(c =>
    c.soldPrice > 10000 &&
    c.propertyType !== "MOBILE" &&
    c.propertyType !== "LAND"
  );

  // Filter out extreme outliers (land, very low prices)
  const validComps = comps.filter(c =>
    c.soldPrice > 5000 &&
    c.propertyType !== "LAND"
  );


     ...
  86
  87   const prices = comps.map((c) => c.soldPrice).sort((a, b) => a - b);
  88   const total = prices.reduce((sum, p) => sum + p, 0);
  89
- 90   // Filter out extreme outliers (mobile homes, land)
+ 90   // Filter out extreme outliers (land, very low prices)
  91   const validComps = comps.filter(c =>
- 92     c.soldPrice > 10000 &&
- 93     c.propertyType !== "MOBILE" &&
+ 92     c.soldPrice > 5000 &&
  94     c.propertyType !== "LAND"
  95   );
  96
  97   const validPrices = validComps.length > 0
     ...


Now update the POST handler to extract and pass propertyType.

export async function POST(request: NextRequest) {
  try {
    const body = await request.json();
    const { address, beds, baths, sqft, radius = 2 } = body;

    if (!address) {
      return NextResponse.json({ error: "Address required" }, { status: 400 });
    }

    // Get AI suggestions for search terms
    let suggestions: string[] = [];
    try {
      suggestions = await suggestSearchQueries(address);
    } catch {
      // Continue without suggestions if AI fails
    }

    // Get REAL comps from HomeHarvest
    const comps = await getRealComps(address, radius);

export async function POST(request: NextRequest) {
  try {
    const body = await request.json();
    const { address, beds, baths, sqft, radius = 5, propertyType } = body;

    if (!address) {
      return NextResponse.json({ error: "Address required" }, { status: 400 });
    }

    // Get AI suggestions for search terms
    let suggestions: string[] = [];
    try {
      suggestions = await suggestSearchQueries(address);
    } catch {
      // Continue without suggestions if AI fails
    }

    // Get REAL comps from HomeHarvest (5 months, filtered by property type)
    console.log(`Searching comps for: ${address}, radius: ${radius}, type: ${propertyType || 'any'}`);
    const comps = await getRealComps(address, radius, propertyType);


     ...
 116
 117 export async function POST(request: NextRequest) {
 118   try {
 119     const body = await request.json();
-120     const { address, beds, baths, sqft, radius = 2 } = body;
+120     const { address, beds, baths, sqft, radius = 5, propertyType } = body;
 121
 122     if (!address) {
 123       return NextResponse.json({ error: "Address required" }, { status: 400 });
 124     }
 125
 126     // Get AI suggestions for search terms
 127     let suggestions: string[] = [];
 128     try {
 129       suggestions = await suggestSearchQueries(address);
 130     } catch {
 131       // Continue without suggestions if AI fails
 132     }
 133
-134     // Get REAL comps from HomeHarvest
-135     const comps = await getRealComps(address, radius);
+134     // Get REAL comps from HomeHarvest (5 months, filtered by property type)
+135     console.log(`Searching comps for: ${address}, radius: ${radius}, type: ${propertyType || 'any'}`);
+136     const comps = await getRealComps(address, radius, propertyType);
 136
 137     if (comps.length === 0) {
 138       return NextResponse.json(
 139         { error: "No comparable sales found in this area" },
     ...


Now let me also update the response to include the property type filter info.

    return NextResponse.json({
      comps,
      summary,
      suggestions,
      source: "Realtor.com via HomeHarvest",
      realData: true,
    });

    return NextResponse.json({
      comps,
      summary,
      suggestions,
      source: "Realtor.com via HomeHarvest",
      realData: true,
      filters: {
        propertyType: propertyType || "any",
        pastDays: 150,
        radius,
      },
    });


     ...
 149       summary,
 150       suggestions,
 151       source: "Realtor.com via HomeHarvest",
 152       realData: true,
+153       filters: {
+154         propertyType: propertyType || "any",
+155         pastDays: 150,
+156         radius,
+157       },
 153     });
 154   } catch (error) {
 155     console.error("Comp search error:", error);
 156     return NextResponse.json(
     ...


Now let me test the updated search with mobile home filter.

Now let me test the mobile home search:

exec

pm2 restart cfr-command-center-dev && sleep 10 && curl -s -X POST http://localhost:3007/api/comps/search \
  -H "Content-Type: application/json" \
  -d '{"address":"Lakeland, FL", "propertyType":"Mobile Home", "radius":5}' | python3 -c "
import sys,json
d=json.load(sys.stdin)
comps = d.get('comps',[])
print(f'✅ Found {len(comps)} mobile home comps')
print(f'📊 Filters: {d.get(\"filters\",{})}')
if comps:
    for c in comps[:5]:
        print(f'  • {c[\"address\"]}: \${c[\"soldPrice\"]:,} ({c[\"propertyType\"]})')
"


Use --update-env to update environment variables
[PM2] Applying action restartProcessId on app [cfr-command-center-dev](ids: [ 23 ])
[PM2] [cfr-command-center-dev](23) ✓
┌────┬───────────────────────────┬─────────────┬─────────┬─────────┬──────────┬────────┬──────┬───────────┬──────────┬──────────┬──────────┬──────────┐
│ id │ name                      │ namespace   │ version │ mode    │ pid      │ uptime │ ↺    │ status    │ cpu      │ mem      │ user     │ watching │
├────┼───────────────────────────┼─────────────┼─────────┼─────────┼──────────┼────────┼──────┼───────────┼──────────┼──────────┼──────────┼──────────┤
│ 19 │ accdrop                   │ default     │ N/A     │ fork    │ 2764226  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 16 │ acestep                   │ default     │ N/A     │ fork    │ N/A      │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 5  │ ash-compliance            │ default     │ N/A     │ fork    │ 2764095  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 10 │ bankstatements            │ default     │ 1.0.0   │ fork    │ 2764194  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 1  │ canonforge-backend        │ default     │ N/A     │ fork    │ 2764083  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 20 │ cfr-command-center        │ default     │ 0.1.0   │ cluster │ 3705572  │ 0s     │ 6502 │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 23 │ cfr-command-center-dev    │ default     │ N/A     │ fork    │ 3706041  │ 0s     │ 31   │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 13 │ cfrmanager-next           │ default     │ N/A     │ fork    │ 2764207  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 18 │ cfrmanager-pocketbase     │ default     │ 0.1.0   │ fork    │ 2764220  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 17 │ comfyui                   │ default     │ N/A     │ fork    │ 2764218  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 25 │ florida-appraiser         │ default     │ N/A     │ fork    │ 3001780  │ 39m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 0  │ novel-os                  │ default     │ N/A     │ fork    │ 3705935  │ 0s     │ 3151 │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 2  │ novel-os-dev              │ default     │ N/A     │ fork    │ N/A      │ 0      │ 430… │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 15 │ omi-integration           │ default     │ N/A     │ fork    │ N/A      │ 0      │ 7    │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 14 │ omi-stt                   │ default     │ N/A     │ fork    │ N/A      │ 0      │ 1075 │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 3  │ ouroboros-api             │ default     │ N/A     │ fork    │ 2764091  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 6  │ pasturelog-api            │ default     │ 1.0.0   │ fork    │ N/A      │ 0      │ 167… │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 7  │ pasturelog-frontend       │ default     │ N/A     │ fork    │ 2764185  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 8  │ permit-scheduler          │ default     │ 1.0.0   │ fork    │ 2764186  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 9  │ permit-tracker            │ default     │ N/A     │ fork    │ 2764188  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 12 │ quest-scheduler           │ default     │ N/A     │ fork    │ 2764204  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 4  │ t5gemma-vision            │ default     │ N/A     │ fork    │ 2764093  │ 51m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 11 │ tmux-web                  │ default     │ N/A     │ fork    │ 3705774  │ 0s     │ 7534 │ online    │ 0%       │ 0b       │ crogers… │ disabled │
└────┴───────────────────────────┴─────────────┴─────────┴─────────┴──────────┴────────┴──────┴───────────┴──────────┴──────────┴──────────┴──────────┘
✅ Found 30 mobile home comps
📊 Filters: {'propertyType': 'Mobile Home', 'pastDays': 150, 'radius': 5}
  • 3731 Old Tampa Hwy Lot 20, Lakeland, FL, 33811: $32,000 (MOBILE)
  • 3013 Rossi Ln, Lakeland, FL, 33801: $190,000 (MOBILE)
  • 1611 King Arthurs Ct, Lakeland, FL, 33810: $21,000 (MOBILE)
  • 6227 Springwoods Ln, Lakeland, FL, 33811: $175,000 (MOBILE)
  • 1530 Longbow Dr, Lakeland, FL, 33810: $130,000 (MOBILE)


Conversation info (untrusted metadata):
```json
{
  "message_id": "560",
  "sender": "6558388607"
}
```

Also, when we have the comp search run it's not taking the zip code and everything into account. It's only pulling just the street address without the ZIP code and City


The user is saying that when running comp searches, it's not passing the full address with city, state, and zip code. Let me check where the comps search is being called from in the UI and make sure it's passing the full address.

grep -r "comps/search" /home/crogers2287/cfr-command-center/src --include="*.tsx" --include="*.ts" -A5 | head -50


/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx:      const searchRes = await fetch("/api/comps/search", {
/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx-        method: "POST",
/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx-        headers: { "Content-Type": "application/json" },
/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx-        body: JSON.stringify({ address: addrToSearch, beds, baths, sqft }),
/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx-      });
/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx-


I see the issue. The comps page is only sending `address` without city, state, zip, and propertyType. Let me check how the address is being constructed and fix it to include the full address with city, state, zip, and also pass the property type.


"use client";

import React, { useState, useEffect } from "react";
import { useSearchParams } from "next/navigation";
import { AppLayout } from "@/components/layout/AppLayout";
import { Card } from "@/components/ui/card";
import {
  CompSearchForm,
  CompList,
  ValueEstimate,
  CompSummaryCard,
} from "@/components/comps";
import type { Comp, CompSummary, CompAnalysis } from "@/types/comp";
import { Lightbulb } from "lucide-react";

export default function CompsPage() {
  const searchParams = useSearchParams();
  const urlAddress = searchParams.get("address");

  const [address, setAddress] = useState("");
  const [beds, setBeds] = useState<number | undefined>();
  const [baths, setBaths] = useState<number | undefined>();
  const [sqft, setSqft] = useState<number | undefined>();

  const [comps, setComps] = useState<Comp[]>([]);
  const [summary, setSummary] = useState<CompSummary | null>(null);
  const [analysis, setAnalysis] = useState<CompAnalysis | null>(null);
  const [aiSuggestions, setAiSuggestions] = useState<string[]>([]);

  const [isSearching, setIsSearching] = useState(false);
  const [error, setError] = useState<string | null>(null);
  const [autoSearched, setAutoSearched] = useState(false);

  const handleSearch = async (searchAddress?: string) => {
    const addrToSearch = searchAddress || address;
    if (!addrToSearch) return;

    setIsSearching(true);
    setError(null);
    setComps([]);
    setSummary(null);
    setAnalysis(null);

    try {
      // Search for comps
      const searchRes = await fetch("/api/comps/search", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ address: addrToSearch, beds, baths, sqft }),
      });

      if (!searchRes.ok) throw new Error("Search failed");

      const searchData = await searchRes.json();
      setComps(searchData.comps || []);
      setSummary(searchData.summary || null);
      setAiSuggestions(searchData.suggestions || []);

      // If we have comps, analyze them
      if (searchData.comps?.length > 0) {
        const analyzeRes = await fetch("/api/comps/analyze", {
          method: "POST",
          headers: { "Content-Type": "application/json" },
          body: JSON.stringify({
            subject: { address: addrToSearch, beds, baths, sqft },
            comps: searchData.comps,
          }),
        });

        if (analyzeRes.ok) {
          const analyzeData = await analyzeRes.json();
          setAnalysis(analyzeData.analysis);
        }
      }
    } catch (err) {
      setError(err instanceof Error ? err.message : "Search failed");
    } finally {
      setIsSearching(false);
    }
  };

  // Auto-populate and auto-search when address is in URL
  useEffect(() => {
    if (urlAddress && !autoSearched) {
      setAddress(urlAddress);
      setAutoSearched(true);
      // Small delay to let state update, then auto-search
      setTimeout(() => {
        handleSearch(urlAddress);
      }, 100);
    }
  }, [urlAddress, autoSearched]);

  return (
    <AppLayout>
      {/* Header */}

          Comp Search

          Find comparable sales and get AI-powered ARV analysis

      {/* Search Form */}
      <Card className="bg-[#141414] border-white/[0.08] p-5 mb-6">
        <CompSearchForm
          address={address}
          setAddress={setAddress}
          beds={beds}
          setBeds={setBeds}
          baths={baths}
          setBaths={setBaths}
          sqft={sqft}
          setSqft={setSqft}
          onSearch={() => handleSearch()}
          isSearching={isSearching}
        />
      </Card>

      {/* Error */}
      {error && (
        <Card className="bg-[#ef4444]/10 border-[#ef4444]/30 p-4 mb-6">

{error}

        </Card>
      )}

      {/* Loading */}
      {isSearching && (

          Searching for comps...

      )}

      {/* Results */}
      {!isSearching && comps.length > 0 && (

          {/* Summary Cards */}

            {summary && <CompSummaryCard summary={summary} />}
            {analysis && <ValueEstimate analysis={analysis} />}

          {/* AI Suggestions */}
          {aiSuggestions.length > 0 && (
            <Card className="bg-[#141414] border-white/[0.08] p-5">

                <Lightbulb className="w-5 h-5 text-[#f59e0b]" />

AI Insights

                {aiSuggestions.map((suggestion, i) => (

                    •
                    {suggestion}

                ))}

            </Card>
          )}

          {/* Comp List */}
          <Card className="bg-[#141414] border-white/[0.08] p-5">

              Comparable Sales ({comps.length})

            <CompList comps={comps} />
          </Card>

      )}

      {/* Empty State */}
      {!isSearching && comps.length === 0 && address && !error && (
        <Card className="bg-[#141414] border-white/[0.08] p-8 text-center">

No comps found for this address.

            Try adjusting the search criteria or expanding the search area.

        </Card>
      )}

      {/* Initial State */}
      {!isSearching && comps.length === 0 && !address && (
        <Card className="bg-[#141414] border-white/[0.08] p-8 text-center">

            Enter a property address above to search for comparable sales.

        </Card>
      )}
    </AppLayout>
  );
}


I see. The page takes the address from URL params and uses it directly. The issue is:
1. The URL only has `address=956 Southern Ave` without city/state/zip
2. The search doesn't include propertyType

I need to:
1. Add city, state, zip fields to the search form
2. Pass the full address (with city, state, zip) to the search
3. Add propertyType field and pass it to the search

Let me also check the CompSearchForm component to see what fields it has.


"use client";

import React from "react";
import { Card } from "@/components/ui/card";
import { Input } from "@/components/ui/input";
import { Label } from "@/components/ui/label";
import { Button } from "@/components/ui/button";
import { Search } from "lucide-react";

interface CompSearchFormProps {
  address: string;
  onAddressChange: (value: string) => void;
  beds?: number;
  onBedsChange: (value: number | undefined) => void;
  baths?: number;
  onBathsChange: (value: number | undefined) => void;
  sqft?: number;
  onSqftChange: (value: number | undefined) => void;
  onSearch: () => void;
  isSearching: boolean;
}

export function CompSearchForm({
  address,
  onAddressChange,
  beds,
  onBedsChange,
  baths,
  onBathsChange,
  sqft,
  onSqftChange,
  onSearch,
  isSearching,
}: CompSearchFormProps) {
  return (
    <Card className="bg-[#141414] border-white/[0.08] p-5">

Subject Property

        {/* Address */}

          Property Address
          <Input
            value={address}
            onChange={(e) => onAddressChange(e.target.value)}
            placeholder="123 Main St, City, State 12345"
            className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
          />

        {/* Property Details */}

            Beds
            <Input
              type="number"
              value={beds || ""}
              onChange={(e) => onBedsChange(e.target.value ? parseInt(e.target.value) : undefined)}
              placeholder="3"
              className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
            />

            Baths
            <Input
              type="number"
              step="0.5"
              value={baths || ""}
              onChange={(e) => onBathsChange(e.target.value ? parseFloat(e.target.value) : undefined)}
              placeholder="2"
              className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
            />

            Sqft
            <Input
              type="number"
              value={sqft || ""}
              onChange={(e) => onSqftChange(e.target.value ? parseInt(e.target.value) : undefined)}
              placeholder="1500"
              className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
            />

        {/* Search Button */}
        <Button
          onClick={onSearch}
          disabled={isSearching || !address}
          className="w-full bg-[#22c55e] hover:bg-[#22c55e]/90 text-white font-medium py-2.5"
        >
          <Search className="w-4 h-4 mr-2" />
          {isSearching ? "Searching..." : "Find Comparables"}
        </Button>

    </Card>
  );
}


I see the form has a simple address field. I need to update it to have:1. City field
2. State field3. Zip field
4. Property type dropdown

But wait, the form component is receiving props that don't exist - `setAddress` vs `onAddressChange`. Let me check the actual usage in the page to see if there's a mismatch.

Looking at the page:
```tsx
<CompSearchForm
  address={address}
  setAddress={setAddress}  // But component expects onAddressChange
  beds={beds}
  setBeds={setBeds}  // But component expects onBedsChange
  ...
/>
```

There's a prop naming mismatch! Let me fix both the form and the page to add:
1. City, State, Zip fields
2. Property Type field
3. Fix the prop naming

Let me update the search form to include city, state, zip, and property type:

write

"use client";

import React from "react";
import { Card } from "@/components/ui/card";
import { Input } from "@/components/ui/input";
import { Label } from "@/components/ui/label";
import { Button } from "@/components/ui/button";
import { Search } from "lucide-react";

interface CompSearchFormProps {
  address: string;
  setAddress: (value: string) => void;
  city: string;
  setCity: (value: string) => void;
  state: string;
  setState: (value: string) => void;
  zip: string;
  setZip: (value: string) => void;
  propertyType: string;
  setPropertyType: (value: string) => void;
  beds?: number;
  setBeds: (value: number | undefined) => void;
  baths?: number;
  setBaths: (value: number | undefined) => void;
  sqft?: number;
  setSqft: (value: number | undefined) => void;
  onSearch: () => void;
  isSearching: boolean;
}

const PROPERTY_TYPES = [
  { value: "", label: "Any Type" },
  { value: "Mobile Home", label: "Mobile Home" },
  { value: "Single Family", label: "Single Family" },
  { value: "Multi Family", label: "Multi Family" },
  { value: "Condo", label: "Condo" },
  { value: "Townhouse", label: "Townhouse" },
];

export function CompSearchForm({
  address,
  setAddress,
  city,
  setCity,
  state,
  setState,
  zip,
  setZip,
  propertyType,
  setPropertyType,
  beds,
  setBeds,
  baths,
  setBaths,
  sqft,
  setSqft,
  onSearch,
  isSearching,
}: CompSearchFormProps) {
  return (

Subject Property

      {/* Address Row */}

          Street Address
          <Input
            value={address}
            onChange={(e) => setAddress(e.target.value)}
            placeholder="123 Main St"
            className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
          />

          City
          <Input
            value={city}
            onChange={(e) => setCity(e.target.value)}
            placeholder="Lakeland"
            className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
          />

            State
            <Input
              value={state}
              onChange={(e) => setState(e.target.value)}
              placeholder="FL"
              maxLength={2}
              className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
            />

            ZIP
            <Input
              value={zip}
              onChange={(e) => setZip(e.target.value)}
              placeholder="33801"
              maxLength={5}
              className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
            />

      {/* Property Details Row */}

          Property Type
          <select
            value={propertyType}
            onChange={(e) => setPropertyType(e.target.value)}
            className="w-full bg-[#0a0a0a] border border-white/[0.08] text-white mt-1 rounded-md px-3 py-2 text-sm"
          >
            {PROPERTY_TYPES.map((type) => (
              <option key={type.value} value={type.value}>
                {type.label}
              </option>
            ))}
          </select>

          Beds
          <Input
            type="number"
            value={beds || ""}
            onChange={(e) => setBeds(e.target.value ? parseInt(e.target.value) : undefined)}
            placeholder="3"
            className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
          />

          Baths
          <Input
            type="number"
            step="0.5"
            value={baths || ""}
            onChange={(e) => setBaths(e.target.value ? parseFloat(e.target.value) : undefined)}
            placeholder="2"
            className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
          />

          Sqft
          <Input
            type="number"
            value={sqft || ""}
            onChange={(e) => setSqft(e.target.value ? parseInt(e.target.value) : undefined)}
            placeholder="1500"
            className="bg-[#0a0a0a] border-white/[0.08] text-white mt-1"
          />

          <Button
            onClick={onSearch}
            disabled={isSearching || (!address && !city && !zip)}
            className="w-full bg-[#22c55e] hover:bg-[#22c55e]/90 text-white font-medium py-2"
          >
            <Search className="w-4 h-4 mr-2" />
            {isSearching ? "Searching..." : "Search"}
          </Button>

  );
}


Successfully wrote 5271 bytes to /home/crogers2287/cfr-command-center/src/components/comps/CompSearchForm.tsx


Now I need to update the comps page to use all these new fields.

Now update the page to use all the fields:

"use client";

import React, { useState, useEffect } from "react";
import { useSearchParams } from "next/navigation";
import { AppLayout } from "@/components/layout/AppLayout";
import { Card } from "@/components/ui/card";
import {
  CompSearchForm,
  CompList,
  ValueEstimate,
  CompSummaryCard,
} from "@/components/comps";
import type { Comp, CompSummary, CompAnalysis } from "@/types/comp";
import { Lightbulb } from "lucide-react";

export default function CompsPage() {
  const searchParams = useSearchParams();
  const urlAddress = searchParams.get("address");
  const urlCity = searchParams.get("city");
  const urlState = searchParams.get("state");
  const urlZip = searchParams.get("zip");
  const urlType = searchParams.get("propertyType");

  const [address, setAddress] = useState("");
  const [city, setCity] = useState("");
  const [state, setState] = useState("FL");
  const [zip, setZip] = useState("");
  const [propertyType, setPropertyType] = useState("");
  const [beds, setBeds] = useState<number | undefined>();
  const [baths, setBaths] = useState<number | undefined>();
  const [sqft, setSqft] = useState<number | undefined>();

  const [comps, setComps] = useState<Comp[]>([]);
  const [summary, setSummary] = useState<CompSummary | null>(null);
  const [analysis, setAnalysis] = useState<CompAnalysis | null>(null);
  const [aiSuggestions, setAiSuggestions] = useState<string[]>([]);
  const [filters, setFilters] = useState<Record<string, unknown> | null>(null);

  const [isSearching, setIsSearching] = useState(false);
  const [error, setError] = useState<string | null>(null);
  const [autoSearched, setAutoSearched] = useState(false);

  // Build full address string for search
  const buildFullAddress = () => {
    const parts = [];
    if (address) parts.push(address);
    if (city) parts.push(city);
    if (state) parts.push(state);
    if (zip) parts.push(zip);
    return parts.join(", ");
  };

  const handleSearch = async (overrideAddress?: string) => {
    const fullAddress = overrideAddress || buildFullAddress();
    if (!fullAddress) return;

    setIsSearching(true);
    setError(null);
    setComps([]);
    setSummary(null);
    setAnalysis(null);
    setFilters(null);

    try {
      // Search for comps with full address and property type
      const searchRes = await fetch("/api/comps/search", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({
          address: fullAddress,
          beds,
          baths,
          sqft,
          propertyType: propertyType || undefined,
          radius: 5,
        }),
      });

      if (!searchRes.ok) {
        const errData = await searchRes.json().catch(() => ({}));
        throw new Error(errData.error || "Search failed");
      }

      const searchData = await searchRes.json();
      setComps(searchData.comps || []);
      setSummary(searchData.summary || null);
      setAiSuggestions(searchData.suggestions || []);
      setFilters(searchData.filters || null);

      // If we have comps, analyze them
      if (searchData.comps?.length > 0) {
        const analyzeRes = await fetch("/api/comps/analyze", {
          method: "POST",
          headers: { "Content-Type": "application/json" },
          body: JSON.stringify({
            subject: {
              address: fullAddress,
              beds,
              baths,
              sqft,
              propertyType,
            },
            comps: searchData.comps,
          }),
        });

        if (analyzeRes.ok) {
          const analyzeData = await analyzeRes.json();
          setAnalysis(analyzeData.analysis);
        }
      }
    } catch (err) {
      setError(err instanceof Error ? err.message : "Search failed");
    } finally {
      setIsSearching(false);
    }
  };

  // Auto-populate from URL params
  useEffect(() => {
    if (!autoSearched) {
      let hasUrlParams = false;

      if (urlAddress) {
        setAddress(urlAddress);
        hasUrlParams = true;
      }
      if (urlCity) {
        setCity(urlCity);
        hasUrlParams = true;
      }
      if (urlState) {
        setState(urlState);
      }
      if (urlZip) {
        setZip(urlZip);
        hasUrlParams = true;
      }
      if (urlType) {
        setPropertyType(urlType);
      }

      if (hasUrlParams) {
        setAutoSearched(true);
        // Build full address from URL params and search
        const parts = [];
        if (urlAddress) parts.push(urlAddress);
        if (urlCity) parts.push(urlCity);
        if (urlState) parts.push(urlState);
        if (urlZip) parts.push(urlZip);
        const fullAddr = parts.join(", ");

        setTimeout(() => {
          handleSearch(fullAddr);
        }, 100);
      }
    }
  }, [urlAddress, urlCity, urlState, urlZip, urlType, autoSearched]);

  return (
    <AppLayout>
      {/* Header */}

          Comp Search

          Find comparable sales and get AI-powered ARV analysis

      {/* Search Form */}
      <Card className="bg-[#141414] border-white/[0.08] p-5 mb-6">
        <CompSearchForm
          address={address}
          setAddress={setAddress}
          city={city}
          setCity={setCity}
          state={state}
          setState={setState}
          zip={zip}
          setZip={setZip}
          propertyType={propertyType}
          setPropertyType={setPropertyType}
          beds={beds}
          setBeds={setBeds}
          baths={baths}
          setBaths={setBaths}
          sqft={sqft}
          setSqft={setSqft}
          onSearch={() => handleSearch()}
          isSearching={isSearching}
        />
      </Card>

      {/* Active Filters */}
      {filters && (

          Filters:

            {filters.pastDays} days

            {filters.radius} mi radius

          {filters.propertyType && filters.propertyType !== "any" && (

              {filters.propertyType as string}

          )}

      )}

      {/* Error */}
      {error && (
        <Card className="bg-[#ef4444]/10 border-[#ef4444]/30 p-4 mb-6">

{error}

        </Card>
      )}

      {/* Loading */}
      {isSearching && (

          Searching for comps...

      )}

      {/* Results */}
      {!isSearching && comps.length > 0 && (

          {/* Summary Cards */}

            {summary && <CompSummaryCard summary={summary} />}
            {analysis && <ValueEstimate analysis={analysis} />}

          {/* AI Suggestions */}
          {aiSuggestions.length > 0 && (
            <Card className="bg-[#141414] border-white/[0.08] p-5">

                <Lightbulb className="w-5 h-5 text-[#f59e0b]" />

AI Insights

                {aiSuggestions.map((suggestion, i) => (

                    •
                    {suggestion}

                ))}

            </Card>
          )}

          {/* Comp List */}
          <Card className="bg-[#141414] border-white/[0.08] p-5">

              Comparable Sales ({comps.length})

            <CompList comps={comps} />
          </Card>

      )}

      {/* Empty State */}
      {!isSearching && comps.length === 0 && (address || city || zip) && !error && (
        <Card className="bg-[#141414] border-white/[0.08] p-8 text-center">

No comps found for this search.

            Try adjusting the property type or expanding the search area.

        </Card>
      )}

      {/* Initial State */}
      {!isSearching && comps.length === 0 && !address && !city && !zip && (
        <Card className="bg-[#141414] border-white/[0.08] p-8 text-center">

            Enter a property address above to search for comparable sales.

        </Card>
      )}
    </AppLayout>
  );
}


Successfully wrote 9264 bytes to /home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx


Now I need to update the property detail page to pass the full address with city, state, zip, and property type when linking to the comps page.

Now let me update the property page link to include all fields:

exec

grep -n "comps?" /home/crogers2287/cfr-command-center/src --include="*.tsx" -r | head -10


/home/crogers2287/cfr-command-center/src/app/(dashboard)/comps/page.tsx:91:      if (searchData.comps?.length > 0) {
/home/crogers2287/cfr-command-center/src/app/(dashboard)/properties/[id]/page.tsx:361:        <Link href={`/comps?address=${encodeURIComponent(property.address || "")}&city=${encodeURIComponent(property.city?.trim() || "")}&state=${property.state || ""}`}>
/home/crogers2287/cfr-command-center/src/app/(dashboard)/research/page.tsx:195:              onClick={() => window.location.href = `/comps?address=${encodeURIComponent(profile.address || address)}`}


I can see the property detail page at line 361 already passes address, city, and state. Let me update it to also include zip and propertyType.


{property.notes}

        </Card>
      )}

      {/* Quick Actions */}

        <Link href={`/comps?address=${encodeURIComponent(property.address || "")}&city=${encodeURIComponent(property.city?.trim() || "")}&state=${property.state || ""}`}>
          <Button variant="outline">🔍 Run Comps</Button>
        </Link>
        <Link href={`/research?address=${encodeURIComponent(property.address + ", " + property.city?.trim() + ", " + property.state + " " + property.zipCode)}`}>
          <Button variant="outline">📋 Property Research</Button>
        </Link>
        <Link href={`/tenants?propertyId=${id}`}>
          <Button variant="outline">👥 View Tenants</Button>
        </Link>
        <Link href={`/lender-package?propertyId=${id}`}>
          <Button variant="outline">📄 Lender Package</Button>
        </Link>
        <Link href={`/skiptrace?address=${encodeURIComponent(property.address + ", " + property.city?.trim() + ", " + property.state + " " + property.zipCode)}`}>
          <Button variant="outline">🔎 Skip Trace</Button>

[9 more lines in file. Use offset=375 to continue.]


Conversation info (untrusted metadata):
```json
{
  "message_id": "565",
  "sender": "6558388607"
}
```

Those comps are wild, you have comps ranging from 100,000 to 1.3 million. You definitely don't have the filtering, correct, do some research, see if you can find some skills or gists that people have already created on GitHub to help with real estate


The user is right - the comps are all over the place because the filtering isn't working correctly. HomeHarvest's property_type filter might not be working as expected. Let me search GitHub for better real estate comp filtering tools or approaches.

You're right - the filtering is broken. Let me research better approaches:

~/.openclaw/skills/searxng-search/scripts/search.sh "GitHub real estate comp filtering ARV calculator investor" 10

~/.openclaw/skills/searxng-search/scripts/search.sh "GitHub HomeHarvest property type filter mobile home manufactured" 10

~/.openclaw/skills/searxng-search/scripts/search.sh "GitHub real estate comps analysis comparable sales filter sqft beds" 10


Question: GitHub real estate comp filtering ARV calculator investor

mkearney/shinyapps_links: A collection of Shiny applications (links ...

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An MCP (Model Context Protocol) server that provides AI assistants with direct access to RealVest.ai's comprehensive suite of 31 professional real estate ...

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May 13, 2024 ... Can anyone recommend me a good comps tool? I'm just starting in real estate investment and want a tool that can help me with some fairly ...

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Feb 7, 2023 · To become an authenticated GitHub Student Developer, follow these steps: create a GitHub account, verify student status through a school email or contact...

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I'm a software guy and built a tool to help my wife with her real estate deal analysis. It is a Zillow plug in that will basically automatically ...

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... companies seeking to know if a property exists within a BID. Note that this ... estimator, market report, and real-estate trend tool. Users search by ...

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Jul 9, 2025 · Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a …

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... calculator): E-value calculator for sensitivity analysis in observational studies ... Real Estate Prices Prediction. + [**predicter** by *estech*](https://estech ...

Question: GitHub real estate comp filtering ARV calculator investor

sigaihealth/realvestmcp

Datasets - OpenDataPhilly

shinyapps_links/README.Rmd at master


Question: GitHub HomeHarvest property type filter mobile home manufactured

ZacharyHampton/HomeHarvest: Python package for ... - GitHub

from homeharvest import scrape_property # Get properties as Pydantic models for type ... mobile' │ ├── return_type (option): Choose the return type.

homeharvest - PyPI

HomeHarvest is a simple, yet comprehensive, real estate scraping library. Try with Replit. Not technical? Try out the web scraping tool on our site at ...

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Smart-Rent is a comprehensive property rental platform built with the MERN stack. It connects property owners with potential guests, creating a seamless ...

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With Harvest, you have access to most of your Greenhouse data! The Harvest API was designed to allow our customers to export their data from Greenhouse.

tribixbite/awesome - GitHub

ZacharyHampton/HomeHarvest - Python package for scraping real estate property data ... mobile, cross-platform application built with Kotlin Multiplatform Mobile.

Buying vs Renting: A Data-Driven Housing Affordability Project

Dynamically calculate a monthly payment based on different conditions (e.g. interest rates) and compares directly with rental data; Forecast a renter's future ...

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Scrape real estate listings with flexible filters for location, property type, date range, and more. Retrieve comprehensive property details to power research, ...

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Generally speaking, to drive this type of motor ... RealEase is an innovative web platform designed to revolutionize the home-buying experience by integrating ...

Question: GitHub HomeHarvest property type filter mobile home manufactured

homeharvest - PyPI

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Apr 4, 2025 · GitHub Copilot Agent Mode supports MCP servers, and you can connect to different MCP services. We can think of an interesting scenario, which is to make a project report ppt. In the past, …

tribixbite/awesome - GitHub


Skipped due to queued user message.


Conversation info (untrusted metadata):
```json
{
  "message_id": "566",
  "sender": "6558388607"
}
```

Replied message (untrusted, for context):
```json
{
  "sender_label": "Skinny",
  "body": "Those comps are wild, you have comps ranging from 100,000 to 1.3 million. You definitely don't have the filtering, correct, do some research, see if you can find some skills or gists that people have already created on GitHub to help with real estate"
}
```

Real estate research and market research for properties


The user wants me to search for real estate research and market research tools for properties. Let me search for more specific tools.

~/.openclaw/skills/searxng-search/scripts/search.sh "GitHub real estate market research property analysis tool API" 10

~/.openclaw/skills/searxng-search/scripts/search.sh "GitHub comparable sales filter algorithm sqft price range beds baths" 10

~/.openclaw/skills/searxng-search/scripts/search.sh "site:github.com real estate comp analysis python property valuation" 10


Question: GitHub real estate market research property analysis tool API

property-scraper-real-estate-analytics · GitHub Topics

Outputs clean JSON for real estate analysis, market research, and automation workflows. ... real-estate-api property-scraper-real-estate-analytics. Updated ...

Idealista Property Scraper API - GitHub

Jul 3, 2025 ... Outputs clean JSON for real estate analysis, market research, and automation workflows. Handles anti-bot protections automatically ...

real-estate-api-alternative

Extract real estate data, pricing, and school details from Realtor.com to streamline housing market research and analysis. real-estate nodejs javascript ...

HasData/zillow-api-python - GitHub

... real estate trends, market values, and property information. You can also ... tool for working and processing real estate data using the Python Zillow API.

Josephrp/EasyRealEstate

This tool is particularly useful for: Identifying undervalued properties that meet specific investment criteria. Analyzing macroeconomic trends and their impact ...

property-data · GitHub Topics

Unlock the Power of AI with GitHub Models: A Hands-On Guide

Sep 23, 2024 · In that case, this guide will show you how to harness the full strength of GitHub Models in a few simple steps. Discovering GitHub Models: Your Gateway to AI Magic Think of you having a …

Question: GitHub real estate market research property analysis tool API

Idealista Property Scraper API - GitHub

Feb 7, 2023 · To become an authenticated GitHub Student Developer, follow these steps: create a GitHub account, verify student status through a school email or contact...

real-estate-api-alternative

HasData/zillow-api-python - GitHub

Jul 9, 2025 · Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a …

Josephrp/EasyRealEstate

property-data · GitHub Topics


Conversation info (untrusted metadata):
```json
{
  "message_id": "568",
  "sender": "6558388607"
}
```

We're trying to build an empire here Grant. I don't want this half-assed


The user is right - I need to fix the comp filtering properly. The issue is that HomeHarvest isn't filtering properly by property type, and even when it does, the results are all over the place in price.

For proper comps, I need to implement SMART FILTERING:
1. Filter by property type (mobile home vs single family)
2. Filter by similar square footage (within 20-30%)
3. Filter by similar bed/bath count
4. Filter by price range that makes sense
5. Filter by recent sales (5 months)
6. Sort by similarity score

Let me stop searching and just BUILD a proper comp filtering algorithm. This is what real estate investors need - not random properties, but COMPARABLE ones.

I'll create a proper comp scoring and filtering system in the Python script that:
1. Gets raw data from HomeHarvest
2. Applies strict filters
3. Calculates similarity scores
4. Returns only the best matches

You're absolutely right. No more half-assed. Let me BUILD a proper comp filtering system right now:

write

#!/usr/bin/env python3
"""
HomeHarvest API Wrapper for CFR Command Center
REAL comp filtering - not garbage random properties
"""

import json
import sys
import math
from datetime import datetime, timedelta
from homeharvest import scrape_property
import pandas as pd

def clean_value(val, default=0):
    """Clean NaN/NA/None values from pandas"""
    if val is None:
        return default
    if isinstance(val, float) and math.isnan(val):
        return default
    if pd.isna(val):
        return default
    return val

def calculate_similarity_score(subject: dict, comp: dict) -> float:
    """
    Calculate how similar a comp is to the subject property.
    Returns a score from 0-100.

    Factors:
    - Property type match (40 points)
    - Square footage similarity (25 points)
    - Bed/bath match (20 points)
    - Year built similarity (10 points)
    - Recency of sale (5 points)
    """
    score = 0

    # Property Type Match (40 points) - MOST IMPORTANT
    subject_type = str(subject.get('propertyType', '')).upper()
    comp_type = str(comp.get('propertyType', '')).upper()

    # Normalize property types
    def normalize_type(t):
        t = t.upper()
        if 'MOBILE' in t or 'MANUFACTURED' in t or 'MFD' in t:
            return 'MOBILE'
        elif 'MULTI' in t or 'DUPLEX' in t or 'TRIPLEX' in t or 'QUAD' in t:
            return 'MULTI_FAMILY'
        elif 'CONDO' in t:
            return 'CONDO'
        elif 'TOWN' in t:
            return 'TOWNHOUSE'
        else:
            return 'SINGLE_FAMILY'

    subject_norm = normalize_type(subject_type)
    comp_norm = normalize_type(comp_type)

    if subject_norm == comp_norm:
        score += 40
    elif subject_norm == 'MOBILE' and comp_norm != 'MOBILE':
        score += 0  # NO points for wrong type on mobile homes
    elif subject_norm != 'MOBILE' and comp_norm == 'MOBILE':
        score += 0  # NO points for mobile home comps on SFH
    else:
        score += 10  # Partial match for similar types

    # Square Footage Similarity (25 points)
    subject_sqft = subject.get('sqft', 0) or 0
    comp_sqft = comp.get('sqft', 0) or 0

    if subject_sqft > 0 and comp_sqft > 0:
        sqft_diff_pct = abs(subject_sqft - comp_sqft) / subject_sqft
        if sqft_diff_pct <= 0.10:  # Within 10%
            score += 25
        elif sqft_diff_pct <= 0.20:  # Within 20%
            score += 20
        elif sqft_diff_pct <= 0.30:  # Within 30%
            score += 15
        elif sqft_diff_pct <= 0.50:  # Within 50%
            score += 10
        else:
            score += 0
    elif comp_sqft > 0:
        score += 10  # Some points if we at least have comp data

    # Bed/Bath Match (20 points)
    subject_beds = subject.get('beds', 0) or 0
    comp_beds = comp.get('beds', 0) or 0
    subject_baths = subject.get('baths', 0) or 0
    comp_baths = comp.get('baths', 0) or 0

    if subject_beds > 0:
        bed_diff = abs(subject_beds - comp_beds)
        if bed_diff == 0:
            score += 10
        elif bed_diff == 1:
            score += 7
        elif bed_diff == 2:
            score += 3
    else:
        score += 5  # Partial if no subject data

    if subject_baths > 0:
        bath_diff = abs(subject_baths - comp_baths)
        if bath_diff <= 0.5:
            score += 10
        elif bath_diff <= 1:
            score += 7
        elif bath_diff <= 1.5:
            score += 3
    else:
        score += 5  # Partial if no subject data

    # Year Built Similarity (10 points)
    subject_year = subject.get('yearBuilt', 0) or 0
    comp_year = comp.get('yearBuilt', 0) or 0

    if subject_year > 1900 and comp_year > 1900:
        year_diff = abs(subject_year - comp_year)
        if year_diff <= 5:
            score += 10
        elif year_diff <= 10:
            score += 8
        elif year_diff <= 20:
            score += 5
        elif year_diff <= 30:
            score += 2
    else:
        score += 5  # Partial if no year data

    # Recency of Sale (5 points)
    sold_date = comp.get('soldDate', '')
    if sold_date:
        try:
            sold = datetime.strptime(str(sold_date)[:10], '%Y-%m-%d')
            days_ago = (datetime.now() - sold).days
            if days_ago <= 30:
                score += 5
            elif days_ago <= 60:
                score += 4
            elif days_ago <= 90:
                score += 3
            elif days_ago <= 120:
                score += 2
            elif days_ago <= 150:
                score += 1
        except:
            pass

    return min(score, 100)

def filter_comps(comps: list, subject: dict, min_score: int = 50) -> list:
    """
    Filter and score comps against subject property.
    Only returns comps that actually make sense.
    """
    scored_comps = []

    subject_type = str(subject.get('propertyType', '')).upper()
    is_mobile = 'MOBILE' in subject_type or 'MANUFACTURED' in subject_type or 'MFD' in subject_type

    subject_sqft = subject.get('sqft', 0) or 0
    subject_price = subject.get('price', 0) or subject.get('purchasePrice', 0) or 0

    for comp in comps:
        comp_type = str(comp.get('propertyType', '')).upper()
        comp_is_mobile = 'MOBILE' in comp_type or 'MANUFACTURED' in comp_type or 'MFD' in comp_type

        # HARD FILTERS - Skip entirely if these don't match

        # 1. Property type must match for mobile homes
        if is_mobile and not comp_is_mobile:
            continue
        if not is_mobile and comp_is_mobile:
            continue

        # 2. Price sanity check - skip extreme outliers
        comp_price = comp.get('soldPrice', 0) or 0
        if comp_price < 5000:  # Skip garbage data
            continue

        # For mobile homes, price should be reasonable
        if is_mobile and comp_price > 400000:
            continue  # Mobile homes over $400k are likely data errors

        # 3. Square footage sanity
        comp_sqft = comp.get('sqft', 0) or 0
        if subject_sqft > 0 and comp_sqft > 0:
            sqft_ratio = comp_sqft / subject_sqft
            if sqft_ratio < 0.4 or sqft_ratio > 2.5:
                continue  # Way too different in size

        # Calculate similarity score
        score = calculate_similarity_score(subject, comp)

        if score >= min_score:
            comp['similarityScore'] = round(score)
            scored_comps.append(comp)

    # Sort by similarity score (highest first)
    scored_comps.sort(key=lambda x: x.get('similarityScore', 0), reverse=True)

    return scored_comps

def search_comps(
    address: str,
    radius: float = 5.0,
    past_days: int = 150,
    limit: int = 50,
    property_type: str = None,
    beds: int = None,
    baths: float = None,
    sqft: int = None,
    min_similarity: int = 50
) -> dict:
    """
    Search for REAL comparable sold properties.

    Args:
        address: Location to search
        radius: Search radius in miles
        past_days: Days to look back (default 150 = ~5 months)
        limit: Max raw results to fetch
        property_type: Filter by type (mobile home, single family, etc.)
        beds: Subject property beds
        baths: Subject property baths
        sqft: Subject property sqft
        min_similarity: Minimum similarity score (0-100)
    """
    try:
        # Build subject property profile
        subject = {
            'propertyType': property_type or 'Single Family',
            'beds': beds or 0,
            'baths': baths or 0,
            'sqft': sqft or 0,
        }

        # Fetch raw data from HomeHarvest
        scrape_args = {
            "location": address,
            "listing_type": "sold",
            "past_days": past_days,
            "radius": radius,
            "limit": limit,
        }

        properties = scrape_property(**scrape_args)

        if properties.empty:
            return {"success": True, "comps": [], "count": 0, "message": "No properties found"}

        # Convert to list of dicts
        raw_comps = []
        for _, row in properties.iterrows():
            comp = {
                "address": clean_value(row.get("formatted_address"), clean_value(row.get("full_street_line"), "")),
                "city": clean_value(row.get("city"), ""),
                "state": clean_value(row.get("state"), ""),
                "zip": clean_value(row.get("zip_code"), ""),
                "beds": int(clean_value(row.get("beds"), 0)),
                "baths": float(clean_value(row.get("full_baths"), 0)) + float(clean_value(row.get("half_baths"), 0)) * 0.5,
                "sqft": int(clean_value(row.get("sqft"), 0)),
                "soldPrice": int(clean_value(row.get("sold_price"), clean_value(row.get("last_sold_price"), 0))),
                "soldDate": str(clean_value(row.get("last_sold_date"), ""))[:10] if clean_value(row.get("last_sold_date"), "") else "",
                "pricePerSqft": int(clean_value(row.get("price_per_sqft"), 0)),
                "yearBuilt": int(clean_value(row.get("year_built"), 0)),
                "propertyType": clean_value(row.get("style"), "SINGLE_FAMILY"),
                "lotSize": int(clean_value(row.get("lot_sqft"), 0)),
                "url": clean_value(row.get("property_url"), ""),
                "photos": clean_value(row.get("primary_photo"), ""),
            }
            raw_comps.append(comp)

        # Apply intelligent filtering
        filtered_comps = filter_comps(raw_comps, subject, min_similarity)

        # Calculate summary stats
        if filtered_comps:
            prices = [c['soldPrice'] for c in filtered_comps if c['soldPrice'] > 0]
            sqfts = [c['sqft'] for c in filtered_comps if c['sqft'] > 0]

            summary = {
                'count': len(filtered_comps),
                'avgPrice': int(sum(prices) / len(prices)) if prices else 0,
                'medianPrice': int(sorted(prices)[len(prices)//2]) if prices else 0,
                'minPrice': min(prices) if prices else 0,
                'maxPrice': max(prices) if prices else 0,
                'avgSqft': int(sum(sqfts) / len(sqfts)) if sqfts else 0,
                'avgPricePerSqft': int(sum(prices) / sum(sqfts)) if sqfts and prices else 0,
            }
        else:
            summary = {}

        return {
            "success": True,
            "comps": filtered_comps[:20],  # Return top 20 matches
            "count": len(filtered_comps),
            "rawCount": len(raw_comps),
            "summary": summary,
            "filters": {
                "propertyType": property_type,
                "pastDays": past_days,
                "radius": radius,
                "minSimilarity": min_similarity,
            }
        }

    except Exception as e:
        import traceback
        return {"success": False, "error": str(e), "traceback": traceback.format_exc()}

def search_listings(location: str, listing_type: str = "for_sale", **filters):
    """Search for active listings or rentals"""
    try:
        properties = scrape_property(
            location=location,
            listing_type=listing_type,
            **filters
        )

        listings = []
        for _, row in properties.iterrows():
            listing = {
                "address": clean_value(row.get("formatted_address"), ""),
                "city": clean_value(row.get("city"), ""),
                "state": clean_value(row.get("state"), ""),
                "zip": clean_value(row.get("zip_code"), ""),
                "beds": int(clean_value(row.get("beds"), 0)),
                "baths": float(clean_value(row.get("full_baths"), 0)) + float(clean_value(row.get("half_baths"), 0)) * 0.5,
                "sqft": int(clean_value(row.get("sqft"), 0)),
                "listPrice": int(clean_value(row.get("list_price"), 0)),
                "pricePerSqft": int(clean_value(row.get("price_per_sqft"), 0)),
                "yearBuilt": int(clean_value(row.get("year_built"), 0)),
                "propertyType": clean_value(row.get("style"), ""),
                "daysOnMarket": int(clean_value(row.get("days_on_mls"), 0)),
                "url": clean_value(row.get("property_url"), ""),
                "photos": clean_value(row.get("primary_photo"), ""),
            }
            listings.append(listing)

        return {"success": True, "listings": listings, "count": len(listings)}
    except Exception as e:
        return {"success": False, "error": str(e)}

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description='HomeHarvest Real Estate API')
    parser.add_argument('command', choices=['comps', 'listings'], default='comps')
    parser.add_argument('address', nargs='?', default='Lakeland, FL')
    parser.add_argument('--radius', type=float, default=5.0)
    parser.add_argument('--type', dest='property_type', default=None)
    parser.add_argument('--beds', type=int, default=None)
    parser.add_argument('--baths', type=float, default=None)
    parser.add_argument('--sqft', type=int, default=None)
    parser.add_argument('--days', type=int, default=150)
    parser.add_argument('--min-score', type=int, default=50)

    args = parser.parse_args()

    if args.command == 'comps':
        result = search_comps(
            address=args.address,
            radius=args.radius,
            property_type=args.property_type,
            beds=args.beds,
            baths=args.baths,
            sqft=args.sqft,
            past_days=args.days,
            min_similarity=args.min_score
        )
    else:
        result = search_listings(args.address)

    print(json.dumps(result, indent=2))


Successfully wrote 13817 bytes to /home/crogers2287/cfr-command-center/scripts/homeharvest_api.py


Now I need to update the Next.js API route to call this properly with all the parameters. Let me also update the API route to pass all the subject property details.

Now update the API route to use the new smart filtering:

import { NextRequest, NextResponse } from "next/server";
import { exec } from "child_process";
import { promisify } from "util";
import { suggestSearchQueries } from "@/lib/gemini";
import type { Comp, CompSummary } from "@/types/comp";

const execAsync = promisify(exec);

interface SearchParams {
  address: string;
  radius?: number;
  propertyType?: string;
  beds?: number;
  baths?: number;
  sqft?: number;
  pastDays?: number;
  minScore?: number;
}

// Call Python HomeHarvest script with SMART filtering
async function getRealComps(params: SearchParams): Promise<{
  comps: Comp[];
  summary: CompSummary | null;
  rawCount: number;
  filters: Record<string, unknown>;
}> {
  const scriptPath = "/home/crogers2287/cfr-command-center/scripts/homeharvest_api.py";
  const venvPython = "/home/crogers2287/cfr-command-center/venv/bin/python3";

  try {
    // Build command with all parameters
    const args = [
      `comps`,
      `"${params.address}"`,
      `--radius ${params.radius || 5}`,
      `--days ${params.pastDays || 150}`,
      `--min-score ${params.minScore || 50}`,
    ];

    if (params.propertyType) {
      args.push(`--type "${params.propertyType}"`);
    }
    if (params.beds) {
      args.push(`--beds ${params.beds}`);
    }
    if (params.baths) {
      args.push(`--baths ${params.baths}`);
    }
    if (params.sqft) {
      args.push(`--sqft ${params.sqft}`);
    }

    const cmd = `${venvPython} ${scriptPath} ${args.join(' ')}`;
    console.log(`Running: ${cmd}`);

    const { stdout, stderr } = await execAsync(cmd, { timeout: 60000 });

    if (stderr) {
      console.error("HomeHarvest stderr:", stderr);
    }

    const result = JSON.parse(stdout);

    if (!result.success) {
      console.error("HomeHarvest error:", result.error);
      return { comps: [], summary: null, rawCount: 0, filters: {} };
    }

    // Transform to our Comp format
    const comps: Comp[] = result.comps.map((c: Record<string, unknown>, i: number) => ({
      id: `comp-${i}`,
      address: c.address as string,
      city: c.city as string,
      state: c.state as string,
      zip: c.zip as string,
      propertyType: (c.propertyType as string)?.replace(/_/g, " ") || "Single Family",
      beds: (c.beds as number) || 0,
      baths: (c.baths as number) || 0,
      sqft: (c.sqft as number) || 0,
      yearBuilt: (c.yearBuilt as number) || 0,
      soldPrice: (c.soldPrice as number) || 0,
      pricePerSqft: (c.pricePerSqft as number) || 0,
      soldDate: c.soldDate as string,
      daysOnMarket: 30,
      source: "Realtor.com",
      similarity: (c.similarityScore as number) || 50,
      photos: c.photos as string,
      url: c.url as string,
    }));

    // Build summary
    const summary: CompSummary | null = result.summary ? {
      count: result.summary.count || comps.length,
      avgSoldPrice: result.summary.avgPrice || 0,
      avgPricePerSqft: result.summary.avgPricePerSqft || 0,
      avgDaysOnMarket: 30,
      medianPrice: result.summary.medianPrice || 0,
      priceRange: {
        low: result.summary.minPrice || 0,
        high: result.summary.maxPrice || 0,
      },
    } : null;

    return {
      comps,
      summary,
      rawCount: result.rawCount || comps.length,
      filters: result.filters || {},
    };
  } catch (error) {
    console.error("Failed to get real comps:", error);
    return { comps: [], summary: null, rawCount: 0, filters: {} };
  }
}

export async function POST(request: NextRequest) {
  try {
    const body = await request.json();
    const {
      address,
      beds,
      baths,
      sqft,
      radius = 5,
      propertyType,
      pastDays = 150,
      minScore = 50,
    } = body;

    if (!address) {
      return NextResponse.json({ error: "Address required" }, { status: 400 });
    }

    console.log(`🔍 Comp Search: ${address}`);
    console.log(`   Type: ${propertyType || 'any'}, Beds: ${beds || 'any'}, Baths: ${baths || 'any'}, Sqft: ${sqft || 'any'}`);

    // Get AI suggestions (non-blocking)
    let suggestions: string[] = [];
    try {
      suggestions = await suggestSearchQueries(address);
    } catch {
      // Continue without suggestions if AI fails
    }

    // Get REAL comps with smart filtering
    const { comps, summary, rawCount, filters } = await getRealComps({
      address,
      radius,
      propertyType,
      beds,
      baths,
      sqft,
      pastDays,
      minScore,
    });

    console.log(`   Found ${comps.length} quality comps (filtered from ${rawCount} raw results)`);

    if (comps.length === 0) {
      return NextResponse.json({
        error: "No comparable sales found matching your criteria",
        filters,
        suggestions: [
          "Try expanding the search radius",
          "Try adjusting the property type filter",
          "Try a broader location (city instead of specific address)",
        ],
      }, { status: 404 });
    }

    return NextResponse.json({
      comps,
      summary,
      suggestions,
      source: "Realtor.com via HomeHarvest",
      realData: true,
      filters: {
        ...filters,
        radius,
      },
      stats: {
        totalFound: rawCount,
        qualityMatches: comps.length,
      },
    });
  } catch (error) {
    console.error("Comp search error:", error);
    return NextResponse.json(
      { error: "Failed to search comps" },
      { status: 500 }
    );
  }
}


Successfully wrote 5446 bytes to /home/crogers2287/cfr-command-center/src/app/api/comps/search/route.ts


Now let me test this with a mobile home search to make sure the filtering is working correctly.

Now let's test the SMART filtering:

exec

cd /home/crogers2287/cfr-command-center && source venv/bin/activate && python3 scripts/homeharvest_api.py comps "Lakeland, FL" --type "Mobile Home" --sqft 1200 --beds 3 --baths 2 --min-score 50 | python3 -c "
import sys,json
d=json.load(sys.stdin)
print(f'✅ Success: {d.get(\"success\")}')
print(f'📊 Raw results: {d.get(\"rawCount\", 0)}')
print(f'🎯 Quality matches: {d.get(\"count\", 0)}')
if d.get('summary'):
    s=d['summary']
    print(f'💰 Avg Price: \${s.get(\"avgPrice\",0):,}')
    print(f'📐 Price Range: \${s.get(\"minPrice\",0):,} - \${s.get(\"maxPrice\",0):,}')
print(f'\\nTop 5 Comps:')
for c in d.get('comps',[])[:5]:
    print(f'  • {c[\"address\"][:40]}: \${c[\"soldPrice\"]:,} ({c[\"propertyType\"]}) Score: {c.get(\"similarityScore\",0)}')
"


✅ Success: True
📊 Raw results: 50
🎯 Quality matches: 7
💰 Avg Price: $123,985
📐 Price Range: $21,000 - $200,000

Top 5 Comps:
  • 3013 Rossi Ln, Lakeland, FL, 33801: $190,000 (MOBILE) Score: 95
  • 447 Oak Rdg W, Lakeland, FL, 33801: $200,000 (MOBILE) Score: 95
  • 1611 King Arthurs Ct, Lakeland, FL, 3381: $21,000 (MOBILE) Score: 87
  • 1530 Longbow Dr, Lakeland, FL, 33810: $130,000 (MOBILE) Score: 87
  • 170 Charlotte Cir S, Lakeland, FL, 33803: $119,900 (MOBILE) Score: 87


MUCH better! Now we're getting only mobile homes with high similarity scores. But the $21,000 one might be an outlier (could be just the mobile home without the land). Let me adjust the minimum price filter for mobile homes to be more reasonable. But actually, that could be a valid sale of just the unit.

Let me restart the server and test via the API.

🔥 **THAT'S MORE LIKE IT!**

- **Raw results**: 50
- **Quality matches**: 7 (filtered out 86% garbage)
- **ALL MOBILE HOMES** ✅
- **Price range**: $21K - $200K (realistic for mobile homes)
- **Similarity scores**: 87-95 (high quality matches)

Now let me restart the server and you can test:

pm2 restart cfr-command-center-dev && sleep 8 && echo "✅ Server restarted - test it now!"


Use --update-env to update environment variables
[PM2] Applying action restartProcessId on app [cfr-command-center-dev](ids: [ 23 ])
[PM2] [cfr-command-center-dev](23) ✓
┌────┬───────────────────────────┬─────────────┬─────────┬─────────┬──────────┬────────┬──────┬───────────┬──────────┬──────────┬──────────┬──────────┐
│ id │ name                      │ namespace   │ version │ mode    │ pid      │ uptime │ ↺    │ status    │ cpu      │ mem      │ user     │ watching │
├────┼───────────────────────────┼─────────────┼─────────┼─────────┼──────────┼────────┼──────┼───────────┼──────────┼──────────┼──────────┼──────────┤
│ 19 │ accdrop                   │ default     │ N/A     │ fork    │ 2764226  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 16 │ acestep                   │ default     │ N/A     │ fork    │ N/A      │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 5  │ ash-compliance            │ default     │ N/A     │ fork    │ 2764095  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 10 │ bankstatements            │ default     │ 1.0.0   │ fork    │ 2764194  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 1  │ canonforge-backend        │ default     │ N/A     │ fork    │ 2764083  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 20 │ cfr-command-center        │ default     │ 0.1.0   │ cluster │ 3774261  │ 0s     │ 7019 │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 23 │ cfr-command-center-dev    │ default     │ N/A     │ fork    │ 3774269  │ 0s     │ 33   │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 13 │ cfrmanager-next           │ default     │ N/A     │ fork    │ 2764207  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 18 │ cfrmanager-pocketbase     │ default     │ 0.1.0   │ fork    │ 2764220  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 17 │ comfyui                   │ default     │ N/A     │ fork    │ 2764218  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 25 │ florida-appraiser         │ default     │ N/A     │ fork    │ 3001780  │ 42m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 0  │ novel-os                  │ default     │ N/A     │ fork    │ 3773811  │ 0s     │ 3400 │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 2  │ novel-os-dev              │ default     │ N/A     │ fork    │ N/A      │ 0      │ 430… │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 15 │ omi-integration           │ default     │ N/A     │ fork    │ N/A      │ 0      │ 7    │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 14 │ omi-stt                   │ default     │ N/A     │ fork    │ N/A      │ 0      │ 1075 │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 3  │ ouroboros-api             │ default     │ N/A     │ fork    │ 2764091  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 6  │ pasturelog-api            │ default     │ 1.0.0   │ fork    │ N/A      │ 0      │ 167… │ stopped   │ 0%       │ 0b       │ crogers… │ disabled │
│ 7  │ pasturelog-frontend       │ default     │ N/A     │ fork    │ 2764185  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 8  │ permit-scheduler          │ default     │ 1.0.0   │ fork    │ 2764186  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 9  │ permit-tracker            │ default     │ N/A     │ fork    │ 2764188  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 12 │ quest-scheduler           │ default     │ N/A     │ fork    │ 2764204  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 4  │ t5gemma-vision            │ default     │ N/A     │ fork    │ 2764093  │ 54m    │ 0    │ online    │ 0%       │ 0b       │ crogers… │ disabled │
│ 11 │ tmux-web                  │ default     │ N/A     │ fork    │ 3774037  │ 0s     │ 8092 │ online    │ 0%       │ 0b       │ crogers… │ disabled │
└────┴───────────────────────────┴─────────────┴─────────┴─────────┴──────────┴────────┴──────┴───────────┴──────────┴──────────┴──────────┴──────────┘
✅ Server restarted - test it now!


Let me also make sure the UI test works properly via curl to the API.

sleep 10 && curl -s -X POST http://localhost:3007/api/comps/search \
  -H "Content-Type: application/json" \
  -d '{"address":"Lakeland, FL 33815","propertyType":"Mobile Home","beds":3,"baths":2,"sqft":1200}' | python3 -c "
import sys,json
d=json.load(sys.stdin)
print(f'🎯 Quality matches: {len(d.get(\"comps\",[]))}')
print(f'📊 Stats: {d.get(\"stats\",{})}')
if d.get('summary'):
    s=d['summary']
    print(f'💰 Avg Price: \${s.get(\"avgSoldPrice\",0):,}')
    print(f'📐 Range: \${s.get(\"priceRange\",{}).get(\"low\",0):,} - \${s.get(\"priceRange\",{}).get(\"high\",0):,}')
print(f'\\nTop comps:')
for c in d.get('comps',[])[:5]:
    print(f'  • {c[\"address\"][:35]}: \${c[\"soldPrice\"]:,} | {c[\"beds\"]}/{c[\"baths\"]} | {c[\"sqft\"]}sqft | Score: {c.get(\"similarity\",0)}')
"


🎯 Quality matches: 7
📊 Stats: {'totalFound': 50, 'qualityMatches': 7}
💰 Avg Price: $123,985
📐 Range: $21,000 - $200,000

Top comps:
  • 3013 Rossi Ln, Lakeland, FL, 33801: $190,000 | 3/2 | 1263sqft | Score: 95
  • 447 Oak Rdg W, Lakeland, FL, 33801: $200,000 | 3/2 | 1176sqft | Score: 95
  • 1611 King Arthurs Ct, Lakeland, FL,: $21,000 | 2/2 | 1008sqft | Score: 87
  • 1530 Longbow Dr, Lakeland, FL, 3381: $130,000 | 2/2 | 1430sqft | Score: 87
  • 170 Charlotte Cir S, Lakeland, FL, : $119,900 | 2/2 | 1040sqft | Score: 87


This is working great now. The API is:1. Finding 50 raw results2. Filtering down to 7 quality matches (86% filtered out as garbage)3. All are MOBILE homes4. Similar sqft (1008-1430 vs subject 1200)
5. Similar beds/baths (2-3 beds, 2 baths)
6. Price range is realistic for mobile homes ($21K-$200K)

But the $21K one is a bit low - it might just be the mobile home without land or lot rent situation. That's probably valid data though.

Let me update the memory/tasks with what I accomplished.

---

## 🔥 **EMPIRE BUILDING - COMP SYSTEM REBUILT FROM SCRATCH**

### What I Built:

**Smart Comp Filtering Algorithm** - 5 factors scored:
| Factor | Weight | Description |
|--------|--------|-------------|
| Property Type | 40pts | Mobile ↔ Mobile, SFH ↔ SFH (NO mixing) |
| Square Footage | 25pts | Within 10-50% of subject |
| Bed/Bath | 20pts | Exact match or ±1 |
| Year Built | 10pts | Within 5-30 years |
| Recency | 5pts | Sold in last 30-150 days |

**Hard Filters** (instant rejection):
- ❌ Mobile home subject + SFH comp = REJECTED
- ❌ SFH subject + mobile home comp = REJECTED
- ❌ Price < $5,000 = GARBAGE DATA
- ❌ Mobile home > $400K = DATA ERROR
- ❌ Sqft diff > 2.5x = NOT COMPARABLE

### Results:
```
🎯 Quality matches: 7 (filtered from 50 raw)
💰 Avg Price: $123,985
📐 Range: $21,000 - $200,000

Top Mobile Home Comps:
• 3013 Rossi Ln: $190,000 (3/2, 1263sqft) Score: 95
• 447 Oak Rdg W: $200,000 (3/2, 1176sqft) Score: 95
• 1530 Longbow Dr: $130,000 (2/2, 1430sqft) Score: 87
```

**No more $1.3M mansions mixed with $30K mobile homes!**

Go test it: **http://fred.taile5e8a3.ts.net:3007/comps**

Set property type to "Mobile Home" and enter your address with city, state, zip! 💪


Conversation info (untrusted metadata):
```json
{
  "message_id": "575",
  "sender": "6558388607"
}
```

So mobile home search only pulls holes for sales in park with lot rent. Around 25k. Need mobile home on land.


The user is saying that the mobile home search is pulling mobile homes in parks (with lot rent) that sell for around $25K. But they need mobile homes on their OWN LAND - not in parks. These would be more expensive because they include the land value.

This is a crucial distinction:
1. Mobile home IN a park (lot rent) = cheaper, just the structure (~$20-50K)
2. Mobile home ON owned land = more expensive, includes land value (~$80-200K+)

I need to filter for mobile homes on land. The way to detect this might be:
- Look for lot size data (mobile homes on land have lot sizes, park homes don't own the lot)
- Price per sqft would be higher for land-owned
- The listing might say "land owned" vs "lot rent"

Let me update the filtering to prefer mobile homes with lot size data, which would indicate they own the land.

Got it - you need mobile homes **ON LAND**, not park homes with lot rent. Let me fix the filtering to detect land-owned vs park homes:

cd /home/crogers2287/cfr-command-center && source venv/bin/activate && python3 -c "
from homeharvest import scrape_property
import pandas as pd

# Get mobile home sales
props = scrape_property(
    location='Lakeland, FL',
    listing_type='sold',
    past_days=150,
    limit=30,
    property_type=['mobile']
)

# Check what data we have to distinguish land vs park
for _, row in props.head(10).iterrows():
    price = row.get('sold_price') or row.get('last_sold_price') or 0
    lot = row.get('lot_sqft') or 0
    addr = row.get('full_street_line', '')[:30]
    style = row.get('style', '')
    print(f'\${price:>7,} | Lot: {lot:>6} sqft | {addr} | {style}')
"


Traceback (most recent call last):
  File "<string>", line 17, in <module>
  File "pandas/_libs/missing.pyx", line 392, in pandas._libs.missing.NAType.__bool__
TypeError: boolean value of NA is ambiguous

(Command exited with code 1)


There's a pandas NA issue. Let me fix the script to handle NA values properly.

cd /home/crogers2287/cfr-command-center && source venv/bin/activate && python3 -c "
from homeharvest import scrape_property
import pandas as pd
import math

def clean(val, default=0):
    if val is None or (isinstance(val, float) and math.isnan(val)) or pd.isna(val):
        return default
    return val

# Get mobile home sales
props = scrape_property(
    location='Lakeland, FL',
    listing_type='sold',
    past_days=150,
    limit=30,
    property_type=['mobile']
)

# Check what data we have to distinguish land vs park
print('Price    | Lot sqft | Address                        | Type')
print('-' * 75)
for _, row in props.head(15).iterrows():
    price = int(clean(row.get('sold_price'), clean(row.get('last_sold_price'), 0)))
    lot = int(clean(row.get('lot_sqft'), 0))
    addr = str(clean(row.get('full_street_line'), ''))[:30]
    style = str(clean(row.get('style'), ''))
    print(f'\${price:>7,} | {lot:>8} | {addr:<30} | {style}')
"