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Real Estate Analysis: Market Filtering

Filter residential properties by price, size, and proximity to schools to identify optimal investment opportunities.

Scenario Overview​

Goal: Find single-family homes priced $200k-$400k, >150m², within 1km of highly-rated schools.

Real-World Application:

  • Real estate investors finding properties matching criteria
  • Home buyers searching for family-friendly neighborhoods
  • Real estate agents providing data-driven recommendations
  • Market analysts evaluating property values vs. amenities

Estimated Time: 8 minutes

Difficulty: ⭐ Beginner

Prerequisites​

Required Data​

  1. Residential Properties Layer (points or polygons)

    • Property listings or parcel data
    • Required attributes:
      • price (numeric)
      • area_sqm or living_area (numeric)
      • property_type (text: 'single_family', 'condo', etc.)
    • Optional: bedrooms, bathrooms, year_built

  2. Schools Layer (points)

    • School locations
    • Optional but useful: rating, school_level, name
    • Covers your study area

Sample Data Sources​

Real Estate Data:

  • MLS (Multiple Listing Service) exports
  • Zillow/Trulia data feeds (if available)
  • Municipal property assessment databases
  • OpenStreetMap buildings with tags

Schools Data:

# QGIS QuickOSM plugin
Key: "amenity", Value: "school"
Key: "school", Value: "*"

# Or government data:
- National Center for Education Statistics (USA)
- Department for Education (UK)
- Local education authority databases

Backend Recommendation​

Multi-Backend Comparison - This workflow demonstrates all three:

  • PostgreSQL: Fastest if you have >10k properties
  • Spatialite: Good middle ground for city-scale data
  • OGR: Works everywhere, acceptable performance for <5k properties

Step-by-Step Instructions​

Step 1: Load and Inspect Property Data​

  1. Load properties layer: residential_properties.gpkg

  2. Open Attribute Table (F6)

  3. Verify required fields exist:

    ✓ price (numeric)
    ✓ area_sqm (numeric)
    ✓ property_type (text)

  4. Check data quality:

    Sort by price: Look for unrealistic values (0, NULL, >$10M)
    Sort by area: Check for 0 or NULL values
    Filter property_type: Identify valid categories
Data Cleaning

If you have missing values:

-- Filter out incomplete records FIRST
"price" IS NOT NULL 
AND "area_sqm" > 0 
AND "property_type" IS NOT NULL

Step 2: Apply Basic Attribute Filters​

Using FilterMate:

  1. Open FilterMate panel
  2. Select residential_properties layer
  3. Choose any backend (attribute filtering works equally on all)
  4. Enter expression:
-- Price between $200k and $400k
-- Area greater than 150m²
-- Single-family homes only

"price" >= 200000 
AND "price" <= 400000
AND "area_sqm" >= 150
AND "property_type" = 'single_family'
  1. Click Apply Filter
  2. Review count: "Showing X of Y features"

Expected Result: Properties narrowed down by price, size, and type

Step 3: Add Spatial Filter for School Proximity​

Now add the location-based criterion:

  1. Ensure schools layer is loaded: schools.gpkg
  2. Modify FilterMate expression to add spatial component:
-- Combine attribute filters + spatial proximity
"price" >= 200000 
AND "price" <= 400000
AND "area_sqm" >= 150
AND "property_type" = 'single_family'
AND distance(
  $geometry,
  aggregate(
    layer:='schools',
    aggregate:='collect',
    expression:=$geometry
  )
) <= 1000

Alternative using overlay functions:

-- Same criteria + check any school within 1km exists
"price" BETWEEN 200000 AND 400000
AND "area_sqm" >= 150
AND "property_type" = 'single_family'
AND array_length(
  overlay_within(
    'schools',
    buffer($geometry, 1000)
  )
) > 0
  1. Click Apply Filter
  2. Review results on map (should be concentrated near schools)

Step 4: Refine by School Quality (Optional)​

If your schools layer has rating data:

-- Only properties near highly-rated schools (rating ≥ 8/10)
"price" BETWEEN 200000 AND 400000
AND "area_sqm" >= 150
AND "property_type" = 'single_family'
AND array_max(
  array_foreach(
    overlay_within('schools', buffer($geometry, 1000)),
    attribute(@element, 'rating')
  )
) >= 8

What this does:

  1. Finds all schools within 1km buffer
  2. Gets their rating values
  3. Keeps properties where at least one nearby school has rating ≥8

Step 5: Calculate Distance to Nearest School​

Add field showing exact distance:

  1. Open Field Calculator (Ctrl+I) on filtered layer

  2. Create new field:

    Field name: nearest_school_m
    Type: Decimal (double)
    Precision: 1

    Expression:
    round(
      array_min(
        array_foreach(
          overlay_nearest('schools', $geometry, limit:=1),
          distance(geometry(@element), $geometry)
        )
      ),
      0
    )

  3. Add school name (optional):

    Field name: nearest_school_name
    Type: Text (string)

    Expression:
    attribute(
      overlay_nearest('schools', $geometry, limit:=1)[0],
      'name'
    )

Step 6: Rank Properties by Value​

Create a value score combining multiple factors:

  1. Open Field Calculator

  2. Create calculated field:

    Field name: value_score
    Type: Decimal (double)

    Expression:
    -- Higher score = better value
    -- Weighted factors:
    (400000 - "price") / 1000 * 0.4 +          -- Lower price = better (40% weight)
    ("area_sqm" - 150) * 0.3 +                 -- Larger area = better (30% weight)
    (1000 - "nearest_school_m") * 0.3          -- Closer school = better (30% weight)

  3. Sort by value_score descending to see best deals first

Step 7: Visualize Results​

Color by Distance to School:

  1. Right-click layer → Symbology
  2. Choose Graduated
  3. Value: nearest_school_m
  4. Method: Natural Breaks
  5. Colors: Green (close) → Yellow → Red (far)

Add Labels:

Label with: concat('$', "price"/1000, 'k - ', round("nearest_school_m",0), 'm to school')
Size: 10pt
Buffer: White, 1mm

Step 8: Export Matches for Analysis​

  1. In FilterMate: Click Export Filtered Features

    Format: GeoPackage
    Filename: properties_investment_targets.gpkg
    CRS: WGS84 (for portability)
    Include all attributes: ✓

  2. Export attribute table as spreadsheet:

    Right-click layer → Export → Save Features As
    Format: CSV or XLSX
    Fields: Select relevant columns only

  3. Create simple report (optional):

    # Python Console
    layer = iface.activeLayer()
    features = list(layer.getFeatures())

    print("=== Property Investment Report ===")
    print(f"Matching properties: {len(features)}")
    print(f"Average price: ${sum(f['price'] for f in features)/len(features):,.0f}")
    print(f"Average area: {sum(f['area_sqm'] for f in features)/len(features):.0f} m²")
    print(f"Average distance to school: {sum(f['nearest_school_m'] for f in features)/len(features):.0f} m")
    print(f"Price range: ${min(f['price'] for f in features):,} - ${max(f['price'] for f in features):,}")

Understanding the Results​

What the Filter Shows​

✅ Selected properties: Match ALL criteria:

  • Price: $200,000 - $400,000
  • Size: ≥150m²
  • Type: Single-family home
  • Location: ≤1km from school

❌ Excluded properties: Fail ANY criterion above

Interpreting Property Matches​

High Value Score (>500):

  • Below-market pricing for area
  • Good size for price point
  • Very close to school (family appeal)
  • Action: Priority viewing/offer

Medium Score (250-500):

  • Fair market value
  • Acceptable location
  • Consider other factors (condition, neighborhood)
  • Action: Compare with similar properties

Low Score (<250):

  • May be overpriced
  • Far edge of school proximity
  • Smaller size for price
  • Action: Negotiate or wait for better options

Quality Checks​

  1. Sanity check: View 5-10 random results

    • Verify prices are realistic
    • Measure school distance manually
    • Check property_type matches expectations

  2. Outlier detection:

    -- Find unusually cheap properties (may be errors or great deals)
    "price" / "area_sqm" < 1500  -- Less than $1500/m²

  3. Map patterns: Results should cluster near schools (if not, check CRS)

Best Practices​

Search Strategy Refinement​

Start Broad, Narrow Gradually:

  1. First pass: Apply only price + size filters
  2. Review count: If >100 results, add property_type filter
  3. Add spatial: Apply school proximity
  4. Fine-tune: Add school rating, bedrooms, etc.

Save Filter History:

  • FilterMate automatically saves your expressions
  • Use Filter History panel to compare different criteria sets
  • Save best performing filters as Favorites

Performance Considerations​

Backend Selection Guide:

Properties | Schools | Recommended Backend
-----------|---------|--------------------
< 1,000    | Any     | OGR (simplest)
1k - 10k   | < 100   | Spatialite
> 10k      | Any     | PostgreSQL
Any        | > 500   | PostgreSQL + spatial index

Optimization Tips:

  1. Apply attribute filters first (cheapest):

    -- Good: Attributes first, spatial last
    "price" BETWEEN 200000 AND 400000 AND distance(...) <= 1000

    -- Bad: Spatial first (slower)
    distance(...) <= 1000 AND "price" BETWEEN 200000 AND 400000

  2. Use spatial index (automatic in PostgreSQL, create manually for Spatialite):

    Layer Properties → Create Spatial Index

  3. Simplify school geometry if complex:

    Vector → Geometry → Centroids (schools → points)

Real Estate Best Practices​

Market Analysis:

  • Run this filter weekly to track new listings
  • Compare value_score trends over time
  • Export results with timestamps for historical analysis

Price Adjustment:

-- Adjust for inflation or market changes
"price" * 1.05 BETWEEN 200000 AND 400000  -- +5% market growth

Seasonal Patterns:

-- School proximity more valuable in spring (family moving season)
-- Adjust weight in value_score calculation

Common Issues​

Issue 1: No results or very few results​

Cause: Criteria too strict or data quality issues

Solutions:

1. Relax price range: 150k-500k instead of 200k-400k
2. Reduce minimum area: 120m² instead of 150m²
3. Increase school distance: 2000m instead of 1000m
4. Check for NULL values in attributes
5. Verify schools layer covers same area as properties

Issue 2: Distance calculation returns errors​

Cause: CRS mismatch or layer not found

Solution:

1. Verify schools layer name matches exactly (case-sensitive)
2. Check both layers use same CRS (reproject if needed)
3. Ensure schools layer is in current project
4. Try simpler aggregate approach:
   
   distance(
     $geometry,
     aggregate('schools', 'collect', $geometry)
   ) <= 1000

Issue 3: Performance slow (>30 seconds)​

Cause: Large dataset or complex spatial query

Solutions:

1. Switch to PostgreSQL backend (major speedup)
2. Create spatial index on both layers
3. Pre-filter properties to smaller region:
   "city" = 'Boston' AND [rest of expression]
4. Reduce school query complexity:
   - Use buffer once: overlay_within('schools', buffer($geometry, 1000))
   - Cache in temporary field

Issue 4: Results not near schools visually​

Cause: CRS using degrees instead of meters

Solution:

1. Check layer CRS: Properties → Information
2. If EPSG:4326 (lat/lon), reproject to local UTM:
   Vector → Data Management → Reproject Layer
3. Update distance from 1000 to 0.01 if using degrees (not recommended)

Next Steps​

Advanced Techniques​

1. Multi-Amenity Scoring (schools + parks + shopping):

-- Properties near multiple amenities
array_length(overlay_within('schools', buffer($geometry, 1000))) > 0
AND array_length(overlay_within('parks', buffer($geometry, 500))) > 0
AND array_length(overlay_within('shops', buffer($geometry, 800))) > 0

2. Appreciation Potential (combine demographics):

-- Areas with improving demographics
"median_income_2023" > "median_income_2020" * 1.1  -- 10% income growth
AND distance(centroid, aggregate('new_developments', 'collect', $geometry)) < 2000

3. Commute Time Analysis (requires road network):

Processing → Network Analysis → Service Area
Origin: Properties
Destination: Employment centers
Time limit: 30 minutes

4. Market Comparison (price per sqm by neighborhood):

-- Find properties below neighborhood average
"price" / "area_sqm" < 
  aggregate(
    layer:='all_properties',
    aggregate:='avg',
    expression:="price"/"area_sqm",
    filter:="neighborhood" = attribute(@parent, 'neighborhood')
  ) * 0.9  -- 10% below average

5. Time-Series Tracking (monitor listing duration):

-- Properties on market >30 days (motivated sellers)
"days_on_market" > 30
AND "price_reduced" = 1

Further Learning​

Summary​

✅ You've learned:

  • Combining attribute and spatial filters
  • Distance calculations to nearest features
  • Creating value scores from multiple criteria
  • Exporting filtered results for analysis
  • Managing filter history for different searches

✅ Key techniques:

  • BETWEEN operator for range filtering
  • distance() function for proximity
  • overlay_within() for spatial relationships
  • Field calculator for derived attributes
  • Multi-backend comparison

🎯 Real-world impact: This workflow helps real estate professionals make data-driven decisions, investors identify opportunities quickly, and home buyers find properties matching complex criteria that would take days to research manually.

💡 Pro tip: Save multiple filter variations as Favorites with descriptive names like "Investment: Family Homes Near Schools" or "Budget: Starter Homes Transit Access" to instantly recreate searches.