Using Houski for LLM datasets: Enriching AI with quality property data

Alex Wilkinson

CEO of Houski

2025-03-21

The problem with real estate data for AI

Large language models (LLMs) are only as good as the data they're trained on. When it comes to real estate information, most LLMs suffer from a critical flaw: they've been trained on limited, outdated, or simply incorrect property data. This leads to AI systems that can't reliably answer questions about housing markets, property values, or neighborhood characteristics.

The root cause? The traditional gatekeeping of real estate data by MLS systems has created an artificial scarcity of high-quality property information available for AI training sets. This means most LLMs have a significant blind spot when it comes to accurate, comprehensive real estate knowledge.

Why Houski's open property data API is the solution

Houski's philosophy of making property data accessible, accurate, and comprehensive makes it an ideal source for enhancing LLM training datasets. Here's why:

1. Comprehensive coverage without gatekeepers

Unlike traditional MLS systems that restrict access to realtors, Houski provides data on over 17 million Canadian properties without requiring a real estate license. This creates an opportunity to build AI systems with genuine knowledge about the entire property market, not just listings.

2. Structured, machine-ready data

LLMs need structured, consistent data to learn effectively. Houski's API offers:

• 200+ data points per property
• Standardized field formats
• Consistent coverage across regions
• Clear documentation
• Pricing transparency

This structure makes it straightforward to incorporate property data into AI training pipelines, with fields ranging from basic property characteristics to detailed demographics, property history, and even predictive valuations.

3. Beyond just listings

Traditional real estate data sources focus almost exclusively on properties currently for sale. This creates a significant bias in any AI model trained on such data. Houski's approach is different:

• Data on ALL properties, not just those for sale
• Historical records providing temporal context
• Assessment and permit data for deeper property understanding
• Demographic data for neighborhood context
• Predictive fields generated through their own AI systems

This comprehensive approach helps create LLMs that understand property as a complete ecosystem, not just a transactional marketplace.

Practical applications for LLMs enhanced with Houski data

Factual accuracy for property questions

LLMs enhanced with Houski data can provide factually accurate answers about:

• Property valuations and price trends
• Neighborhood demographics
• Construction details and property characteristics
• Historical property information
• Zoning and land use

This makes them valuable tools for both consumers and professionals seeking quick, accurate property insights.

Real estate analysis and recommendations

With access to Houski's comprehensive data, LLMs can be trained to:

• Identify investment opportunities based on ROI calculations
• Compare neighborhoods using objective metrics
• Analyze price trends and market movements
• Match property characteristics to user preferences
• Make data-driven predictions about property value changes

These capabilities transform LLMs from simple information retrievers into genuine real estate analysis assistants.

Data augmentation for existing models

Even if you're not training an LLM from scratch, Houski's API can enhance existing models through:

• Fine-tuning on property-specific data
• Retrieval-augmented generation using the API as a real-time knowledge source
• Creating specialized property embeddings for improved understanding
• Developing custom evaluation datasets for testing real estate knowledge

Implementation approaches

Direct API integration for retrieval

The simplest approach is to use Houski's API as a knowledge retrieval source. When a user asks a property-related question, the LLM can:

1. Determine what property data is needed
2. Construct the appropriate API query
3. Retrieve the precise data points required
4. Format the information into a natural language response

This approach provides always-current information without requiring constant retraining.

// Example of querying the Houski API from an LLM system
const getPropertyData = async (address, city, province) => {
  const url = new URL('https://api.houski.ca/properties');
  url.searchParams.set('api_key', 'YOUR_API_KEY');
  url.searchParams.set('address', address);
  url.searchParams.set('city', city);
  url.searchParams.set('province_abbreviation', province);
  url.searchParams.set('select', 'estimate_list_price,bedroom,construction_year');

  const response = await fetch(url);
  const data = await response.json();
  
  return data;
}

Training data creation

For more comprehensive integration, you can use Houski's API to generate specialized training datasets:

1. Query properties across diverse regions, types, and price points
2. Generate question-answer pairs about these properties
3. Create comparison examples between similar properties
4. Develop reasoning chains that demonstrate property analysis
5. Build synthetic conversations about property searches

This approach helps LLMs develop deeper domain understanding beyond just factual recall.

Specialized embeddings

Property data often contains numerical and categorical values that benefit from specialized embedding approaches:

1. Create embeddings that preserve the relationships between property features
2. Develop neighborhood-level embeddings that capture community characteristics
3. Generate temporal embeddings to represent historical price trends
4. Build multi-modal embeddings that combine property data with visual information

Cost considerations

One of Houski's advantages is its transparent, usage-based pricing model. For LLM training datasets, costs are predictable:

• Basic property fields cost $0.001 per field
• Expansion datasets (like historical listings) cost $0.01 per row

This means you can build comprehensive datasets with controlled costs, paying only for the specific data points your LLM needs to learn from.

If you would prefer files insetad of utilizing our API, you can contact us for a quote on a bespoke data set.

Conclusion: The future of property-aware AI

As real estate data becomes more accessible through APIs like Houski's, we're entering an era where AI systems can develop genuine expertise in property markets. This represents a significant shift from the gatekept information paradigm that has dominated real estate for decades.

The result will be AI assistants that can provide valuable, accurate property insights without requiring users to navigate complex MLS systems or rely on intermediaries. For developers building the next generation of AI tools, Houski's open approach to property data provides a foundation for creating truly knowledgeable systems.

Want to explore how Houski's property data can enhance your LLM projects? Check out their API documentation to get started. The future of property-aware AI is open, accessible, and built on quality data.