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Building automated valuation models (AVMs) that don't suck: A developer's guide

Photo of Alex Wilkinson
Alex Wilkinson
CEO of Houski
2025-04-21

The real estate industry has a data problem that's costing billions in poor valuation decisions. Most Automated Valuation Models (AVMs) rely on incomplete datasets, simplistic algorithms, and black-box approaches that produce valuations real estate professionals can't trust or explain to clients.

But 2025 marks a turning point. With comprehensive property datasets now available through modern APIs, advanced machine learning techniques becoming more accessible, and growing demand for transparency, developers finally have the tools to build AVMs that work.

This guide will show you how to build accurate, explainable AVMs using modern property data APIs. If you prefer a production-ready solution, Houski's AVM endpoint provides institutional-grade valuations with full accuracy transparency and Canadian market coverage.

Why most AVMs are inaccurate

Before we dive into solutions, let's understand why so many AVMs are useless:

Data Quality Issues

  • Incomplete coverage: Limited to MLS listings or tax assessments, missing 70-80% of market data
  • Inconsistent measurements: Square footage calculated differently across jurisdictions
  • Missing renovations: Recent improvements that significantly impact value go unrecorded
  • Outdated information: Assessment data that's 2-5 years behind current property conditions
  • Location imprecision: Postal code-level data missing micro-location value drivers

Oversimplified modeling approaches

  • Simplistic regression models: Linear models trying to capture non-linear relationships
  • Insufficient location granularity: Treating entire neighborhoods as homogeneous
  • Inadequate feature engineering: Missing interaction effects between variables
  • One-size-fits-all modeling: Using the same approach for mansions and studio apartments
  • Ignorance of market segments: Luxury properties valued like standard homes
  • Temporal blindness: Failing to account for seasonal and cyclical patterns

Opacity without accountability

  • Hidden limitations: Pretending the model works everywhere
  • Missing context: Valuation without market trends or comparable sales
  • No feedback mechanisms: No way to correct obvious errors

Building a better AVM: The foundation

Creating an AVM that doesn't suck starts with getting the fundamentals right:

1. Comprehensive Data Foundation

Modern AVM accuracy starts with comprehensive property data. The Canadian market now has access to detailed information on 17+ million properties through APIs like Houski's platform:

JavaScript code
// Example: Comprehensive property data for AVM training
const getPropertyData = async (propertyId, apiKey) => {
  const url = new URL('https://api.houski.ca/properties');
  url.searchParams.set('api_key', apiKey);
  url.searchParams.set('address', propertyId);
  
  // Select comprehensive features for AVM modeling
  url.searchParams.set('select', [
    'interior_sq_m', 'bedroom', 'den', 'bathroom_full', 'bathroom_half',
    'construction_year', 'lot_area_sq_m', 'property_type',
    'heating_type_first', 'foundation_type', 'roof_material',
    'assessment_value', 'assessment_year', 'latitude', 'longitude'
  ].join(','));
  
  const response = await fetch(url);
  return await response.json();
};

Essential data requirements:

  • Complete coverage: All properties, not just recent sales
  • Rich attributes: 50+ standardized characteristics per property
  • Geographic precision: Exact coordinates for micro-location analysis
  • Construction details: Materials, age, and condition indicators
  • Market context: Recent sales, days on market, price adjustments

2. Sophisticated modeling approach

Modern AVMs require modeling approaches that can handle real estate's complexities:

  • Ensemble methods: Combining multiple model types for better performance
  • Geospatial modeling: Explicit handling of location effects
  • Market segmentation: Separate models for different property types and price points
  • Non-linear relationships: Capturing diminishing returns and threshold effects
  • Temporal dynamics: Accounting for seasonality and market cycles
  • Transfer learning: Leveraging patterns from data-rich areas to improve sparse regions

3. Transparent limitations

Honesty about what your model can and can't do builds credibility:

  • Confidence intervals: Communicating uncertainty appropriate to each prediction
  • Limitation disclosures: Being clear about where the model works best
  • Explainability: Providing insight into which factors drive the valuation
  • Comparable evidence: Showing similar properties that support the estimate
  • Feedback mechanisms: Allowing users to flag problematic valuations
  • Continuous validation: Regular backtesting against actual transactions

Implementation: A developer's roadmap

Here's how to implement these principles in your own AVM. If you'd rather skip the implementation and use a production-ready AVM immediately, our predict endpoint is available with simple API integration.

Stage 1: Data acquisition and preparation

Start by building a comprehensive property database using Houski's API:

JavaScript code
// Houski API call to get detailed property data for modeling
const getPropertyDataForModeling = async () => {
  const url = new URL('https://api.houski.ca/properties');
  
  url.searchParams.set('api_key', 'YOUR_API_KEY');
  
  // Get properties with rich attribute sets
  url.searchParams.set('select', [
    // Location data
    'latitude',
    'longitude',

    // Building details
    'property_type',
    'construction_year',
    'floor_above_ground',
    'basement_type',
    'basement_finish',
    'roof_material_install_year',
    'cooling_type_first',

    // Contextual attributes about the area
    'area_residential_list_price_per_sq_m',
    'area_residential_rent_price_per_sq_m',
    'area_commercial_list_price_per_sq_m',

    // Contextual attibutes about comparable properties in the area
    'area_comparable_list_price_per_sq_m',
    'area_comparable_property_tax_per_sq_m',
    'area_comparable_assessment_value_per_sq_m',

    // Size measurements
    'interior_sq_m',
    'land_area_sq_m',
    'land_frontage_m',
    'land_depth_m',
    
    // Room counts
    'bedroom',
    'den',
    'bathroom_full',
    'bathroom_half',
    
    // Systems
    'heating_type',
    'roof_material',
    'foundation_type',
    
    // Features
    'fireplace',
    
    // Financial aspects
    'maintenance_fee',
    'assessment_value',
    'assessment_year',
    
    // Parking
    'garage_parking_space_first',
    'garage_type_first',

    // Relevant demographic info
    'demographic_income_median_pre_tax',
    'demographic_household_size_1_person_percent',
    'demographic_education_level_bachelors_degree_percent',
    'demographic_dwellings_occupied_percent',
    'demographic_transportation_car_truck_or_van_percent',
    'demographic_municipal_population',
    'demographic_age_median_of_the_population',
  ].join(','));

  // Include listing history for the property
  url.searchParams.set('expand', 'listings');
  
  const response = await fetch(url);
  const data = await response.json();
  
  return data;
}

For training data, collect:

  1. Core property attributes (from Houski's API)

    • Physical characteristics (size, rooms, features)
    • Location coordinates
    • Building information (age, style, construction)
    • Lot details
  2. Transaction history (from Houski's listings expansion)

    • Actual sale prices (not just asking prices)
    • Days on market
    • Price changes during listing
    • Transaction types (standard, foreclosure, etc.)
  3. Location context (from Houski's neighborhood data)

    • School information
    • Crime statistics
    • Walkability scores
    • Transit accessibility
    • Demographic profiles

Stage 2: Feature engineering

Transform raw data into modeling-ready features:

  1. Relative measurements

    • All the Houski area fields and...
    • Size percentile (within market segment)
    • Age relative to surrounding properties
    • Room counts compared to neighborhood averages
  2. Location features

    • Distance to amenities (schools, parks, transit)
    • Neighborhood price trends
    • School quality metrics
    • Crime rates relative to city average
    • Walk/transit/bike scores
  3. Time-based features

    • Days since most recent comparable sale
    • Market velocity indicators
    • Seasonal adjustment factors
    • Price trend by neighborhood
    • Inventory levels over time
  4. Interaction terms

    • Size × neighborhood
    • Age × renovation status
    • Bedroom count × property type
    • Location × school district
    • Condition × neighborhood price point

Stage 3: Model development

Build a sophisticated modeling pipeline:

  1. Market segmentation

    • Separate models for distinct property types
    • Different approaches by price tier
    • Region-specific models where appropriate
    • Specialized handling of unusual properties
  2. Model selection and ensemble creation

    • Gradient boosting for main valuation
    • Neural networks for feature interaction
    • Geospatial models for location effects
    • Linear models for interpretable baselines
    • Random forests for feature importance
  3. Hyperparameter optimization

    • Cross-validation with temporal hold-outs
    • Bayesian optimization of parameters
    • Performance metrics beyond RMSE (consider MAE, MAPE)
    • Validation against different market conditions
  4. Confidence modeling

    • Predictive intervals, not just point estimates
    • Uncertainty quantification based on data availability
    • Property-specific confidence scoring
    • Market condition impact on uncertainty

To see how we've implemented these techniques in practice, explore our model performance metrics on the AVM information page.

Stage 4: Deployment and transparency

Make your model useful and trustworthy:

  1. API design

    • Clear documentation of inputs and outputs
    • Standardized error handling
    • Rate limiting for stability
    • Versioning for model updates
  2. Explainability layer

    • Feature importance for each valuation
    • Comparable properties supporting the estimate
    • Market trends context
    • Key value drivers highlighted
  3. User feedback mechanisms

    • Simple reporting of potential errors
    • Professional override workflows
    • Continuous learning from feedback
    • Performance tracking over time
  4. Dashboard visualization

    • Confidence intervals clearly displayed
    • Historical trend context
    • Similar property comparisons
    • Local market indicators

Our predict endpoint makes it easy to get started with production-ready property valuations, if you want to skip rolling your own AVM.

Supercharging your AVM with geospatial context

One of the most powerful features of modern AVMs is their ability to incorporate rich geospatial context. While basic models might just use zip codes or neighborhoods, truly accurate valuations require granular location data.

Houski's API provides excellent tools for gathering this critical location context through its /geocoding and /map endpoints:

1. Residential property context

Understanding comparable properties in the immediate area is fundamental for any valuation. Here's how to get detailed information on nearby residences:

JavaScript code
// Get residential context for properties within 500m
const getNearbyResidentialProperties = async (latitude, longitude, apiKey) => {
  const url = new URL('https://api.houski.ca/geocoding');
  
  url.searchParams.set('api_key', apiKey);
  url.searchParams.set('latitude', latitude);
  url.searchParams.set('longitude', longitude);
  url.searchParams.set('radius', '0.5');
  url.searchParams.set('shape', 'circle');
  url.searchParams.set('property_type_eq', 'House');
  url.searchParams.set('expand', 'listings');
  url.searchParams.set('select', 'latitude,longitude,interior_sq_m,bedroom,den,bathroom_full,bathroom_half,assessment_value,assessment_year');
  
  const response = await fetch(url);
  const data = await response.json();
  
  return data;
}

This retrieves not just the basic attributes of nearby homes but also their listing history, giving you insight into:

  • Recent comparable sales
  • Price trends in the micro-neighborhood
  • Time on market for similar properties
  • Price adjustments during listings

2. Recent permit activity

Property permits can signal unrealized value or neighborhood transformation that traditional AVMs miss:

JavaScript code
// Get recent permit activity in the area
const getAreaPermitActivity = async (latitude, longitude, apiKey) => {
  const url = new URL('https://api.houski.ca/geocoding');
  
  url.searchParams.set('api_key', apiKey);
  url.searchParams.set('latitude', latitude);
  url.searchParams.set('longitude', longitude);
  url.searchParams.set('radius', '0.5');
  url.searchParams.set('shape', 'circle');
  url.searchParams.set('property_type_in', 'House,Commercial');
  url.searchParams.set('expand', 'permits');
  url.searchParams.set('expand_permit_application_date_gte', '2023-06-01');
  url.searchParams.set('filter_expand_match', 'all');
  
  const response = await fetch(url);
  const data = await response.json();
  
  return data;
}

This provides critical signals that most AVMs miss:

  • Recent renovations that haven't yet been reflected in tax records
  • New commercial developments that may impact residential values
  • Upcoming residential densification
  • Building quality improvements across the neighborhood

3. Amenity proximity

Nearby amenities significantly impact property values but are difficult to quantify without specialized data:

JavaScript code
// Get commercial amenities within 1km
const getNearbyAmenities = async (latitude, longitude, apiKey) => {
  const url = new URL('https://api.houski.ca/geocoding');
  
  url.searchParams.set('api_key', apiKey);
  url.searchParams.set('latitude', latitude);
  url.searchParams.set('longitude', longitude);
  url.searchParams.set('radius', '1');
  url.searchParams.set('shape', 'circle');
  url.searchParams.set('commercial_use_neq', 'Not applicable');
  url.searchParams.set('select', 'building_name,commercial_use,latitude,longitude');
  
  const response = await fetch(url);
  const data = await response.json();
  
  return data;
}

This amenity data helps quantify key value factors:

  • Proximity to grocery stores, restaurants, and retail
  • Access to medical facilities
  • Distance to employment centers
  • Presence of desirable businesses (high-end retail vs. discount stores)

4. Neighborhood market statistics

Using Houski's aggregate endpoint to understand neighborhood-level market statistics:

JavaScript code
// Get median price for specific neighborhood
const getNeighborhoodStats = async (country, province, city, community, apiKey) => {
  const url = new URL('https://api.houski.ca/aggregate');
  
  url.searchParams.set('api_key', apiKey);
  url.searchParams.set('country_abbreviation', country);
  url.searchParams.set('province_abbreviation', province);
  url.searchParams.set('city', city);
  url.searchParams.set('community', community);
  url.searchParams.set('field', 'estimate_list_price');
  url.searchParams.set('aggregation', 'median');
  
  const response = await fetch(url);
  const data = await response.json();
  
  return data;
}

Integrating geospatial context into your model

Here's how to incorporate location data into your AVM:

JavaScript code
// Enhanced AVM with geospatial context
class GeoEnhancedAVM {
  constructor(apiKey) {
    this.apiKey = apiKey;
    // Initialize models and modules
  }
  
  async valuateProperty(propertyId) {
    // Get the core property data
    const propertyData = await this.getPropertyDetails(propertyId);
    
    // Get surrounding properties in parallel
    const [residentialContext, permitContext, amenityContext, neighborhoodStats] = 
      await Promise.all([
        getNearbyResidentialProperties(
          propertyData.latitude, 
          propertyData.longitude, 
          this.apiKey
        ),
        getAreaPermitActivity(
          propertyData.latitude, 
          propertyData.longitude,
          this.apiKey
        ),
        getNearbyAmenities(
          propertyData.latitude, 
          propertyData.longitude,
          this.apiKey
        ),
        getNeighborhoodStats(
          propertyData.country_abbreviation,
          propertyData.province_abbreviation,
          propertyData.city,
          propertyData.community,
          this.apiKey
        )
      ]);
    
    // Calculate geospatial features
    const geoFeatures = {
      // Recent sales metrics
      recentSalesCount: this.countRecentSales(residentialContext),
      avgNearbyPricePerSqM: this.calculateAvgPricePerSqM(residentialContext),
      priceVarianceInArea: this.calculatePriceVariance(residentialContext),
      
      // Recent permit activity
      recentRenovationPermits: this.countRecentPermitsByType(permitContext, 'Renovation'),
      recentNewConstructionPermits: this.countRecentPermitsByType(permitContext, 'New Construction'),
      commercialDevelopmentActivity: this.measureCommercialDevelopment(permitContext),
      
      // Amenity metrics
      groceryStoreCount: this.countNearbyAmenitiesByType(amenityContext, 'Grocery'),
      restaurantCount: this.countNearbyAmenitiesByType(amenityContext, 'Restaurant'),
      retailDensity: this.calculateRetailDensity(amenityContext),
      
      // Neighborhood benchmarks
      medianNeighborhoodPrice: neighborhoodStats.data[0].value,
      priceToNeighborhoodMedian: propertyData.estimate_list_price / neighborhoodStats.data[0].value
    };
    
    // Continue with the regular valuation process, but now with enhanced features
    const allFeatures = {
      ...this.engineerBaseFeatures(propertyData),
      ...geoFeatures
    };
    
    // Run valuation models
    const valuation = this.runValuationModel(allFeatures);
    
    return {
      property_id: propertyId,
      estimated_value: valuation.value,
      confidence_interval: valuation.confidenceInterval,
      value_drivers: valuation.valueDrivers,
      comparables: residentialContext.data.slice(0, 5),
      neighborhood_benchmark: neighborhoodStats.data[0].value
    };
  }
  
  // Helper methods and model handling...
}

By incorporating this rich geospatial data into your AVM, you can capture subtle value factors that traditional models miss:

  • Microneighborhood effects: Properties just blocks apart can have significantly different values
  • Emerging trends: Areas with increasing permit activity often see corresponding value increases
  • Amenity premiums: Quantify the exact premium for proximity to specific amenities
  • Development impact: Measure how new commercial development affects nearby residential values

Real-world example: A modular AVM architecture

Here's a simplified example of how to structure a modern AVM using Houski's property API.

JavaScript code
// A modular AVM architecture using Houski
class ModularAVM {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.modelsBySegment = {};
    this.confidenceModels = {};
    
    // Initialize components
    this.initModels();
  }
  
  async valuateProperty(propertyId) {
    // 1. Gather all necessary data
    const propertyData = await this.fetchPropertyData(propertyId);
    
    // 2. Determine market segment
    const segment = this.classifyPropertySegment(propertyData);
    
    // 3. Gather geospatial context
    const geoContext = await this.fetchGeospatialContext(propertyData);
    
    // 4. Engineer features
    const features = this.engineerFeatures(propertyData, geoContext, segment);
    
    // 5. Get the appropriate model for this segment
    const model = this.modelsBySegment[segment];
    
    // 6. Generate valuation
    const baseValue = model.predict(features);
    
    // 7. Apply adjustments
    const adjustments = this.calculateAdjustments(propertyData, features, segment);
    const adjustedValue = baseValue * adjustments.locationFactor * 
                          adjustments.conditionFactor * adjustments.marketTrendFactor;
    
    // 8. Calculate confidence
    const confidence = this.confidenceModels[segment].calculateConfidence(
      features, adjustedValue, propertyData.data_quality_score
    );
    
    // 9. Find comparable properties
    const comparables = await this.findComparables(propertyData, adjustedValue);
    
    // 10. Return structured result
    return {
      property_id: propertyId,
      estimated_value: adjustedValue,
      confidence_interval: {
        low: adjustedValue * (1 - confidence.margin),
        high: adjustedValue * (1 + confidence.margin)
      },
      value_drivers: this.explainValuation(features, model),
      comparables: comparables,
      market_segment: segment,
      last_updated: new Date()
    };
  }
  
  // Helper methods for model management, data fetching, feature engineering, etc.
  async fetchPropertyData(propertyId) {
    const url = new URL('https://api.houski.ca/properties');
    url.searchParams.set('api_key', this.apiKey);
    url.searchParams.set('property_id_eq', propertyId);
    url.searchParams.set('select', 'all');
    url.searchParams.set('expand', 'listings,assessments');
    
    const response = await fetch(url);
    const data = await response.json();
    
    return data.data[0]; // Return the property data
  }
  
  // Additional methods...
}

This architecture provides:

  • Modularity: Each component handles a specific aspect of valuation
  • Flexibility: Models can be updated independently
  • Transparency: Clear explanation of how the final value is determined
  • Context: Additional information beyond just the value estimate
  • Confidence: Honest communication about uncertainty

Beyond the algorithm: Making your AVM valuable

A great AVM is more than just accurate—it's useful in real-world scenarios. Our predict endpoint was designed with these use cases in mind:

For consumer applications

  • Context matters: Show how the valuation compares to recent trends
  • Visualization helps: Simple charts explaining the estimate
  • Range over precision: Show confidence intervals, not exact figures
  • Comparables build trust: Show similar properties that support the value
  • History tells stories: Track value changes over time

For professional users

  • Adjustment capability: Allow tweaking for property-specific factors
  • Scenario modeling: "What if" analysis for renovations or market changes
  • Batch processing: Valuing entire portfolios efficiently
  • Export functionality: Integration with other tools
  • Override workflows: Professional judgment applied to edge cases

For investment analysis

  • Risk quantification: Understanding valuation uncertainty
  • Return forecasting: Projected value changes based on improvements
  • Portfolio optimization: Identifying over/under valued assets
  • Acquisition targeting: Screening for properties matching investment criteria
  • Risk exposure analysis: Geographic and segment concentration

Conclusion: The future belongs to transparent, accurate AVMs

As real estate technology evolves, the winners won't be those with mysterious black-box valuation models, but those who combine accuracy with transparency. The next generation of AVMs will:

  • Explain the key factors driving each valuation
  • Adapt to changing market conditions in real-time
  • Incorporate both quantitative and qualitative property information
  • Learn continuously from new transactions and feedback

With Houski's comprehensive property API, developers now have access to the data foundation needed to build AVMs that actually work—without requiring enterprise budgets or massive data science teams. And if you'd rather skip the build phase entirely, our predict endpoint provides instant access to a production-ready AVM with transparent accuracy metrics published on our AVM information page.

Ready to build an AVM that doesn't suck? Start with our API documentation to explore the property data that will power your valuation models. Or jump straight to valuations with our predict endpoint.