When to Use Semantic vs. Filter Search

HomeStar offers two ways to search for properties: semantic search (natural language) and traditional filters (dropdowns and ranges). Each excels in different scenarios, and most agents get the best results by combining both. This guide helps you understand when to reach for each tool.

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How Each Method Works

Filter Search: Precision Through Structure

Traditional filters let you specify exact criteria using dropdowns, checkboxes, and range sliders:

• Price range: $200,000 - $500,000
• Bedrooms: Exactly 3 or 4
• Location: Twin Falls city limits
• Property type: Single-family home

Filters search for exact matches in database fields. If a property’s BedroomsTotal field equals 3, it matches your filter. If it’s 2 or 5, it doesn’t appear in results—period.

Strength: Precision and boundaries. You get exactly what you asked for, nothing more, nothing less.

Weakness: You must know the right fields and values to query. Real estate databases use dozens of fields (BedroomsTotal vs. BedroomsAboveGrade vs. Bedrooms), and missing one means missing listings. Also, filters can’t understand concepts like “cozy” or “great for entertaining.”

Semantic Search: Flexibility Through Meaning

Semantic search understands the intent behind your words, not just keywords. Type naturally:

“Modern kitchen with granite countertops and island”

The system finds properties described with:

• “Contemporary design” (synonym for modern)
• “Stone countertops” (granite is a stone)
• “Center island” or “large island” (variations of island)
• “Updated appliances” and “stainless steel” (often paired with modern kitchens)

It uses AI embeddings trained on millions of real estate descriptions to match meaning, even when listings don’t use your exact words.

Strength: Flexibility and intuition. Describe what you want naturally—“family-friendly neighborhood,” “perfect for entertaining,” “great for working from home”—and get listings that match the lifestyle, not just the specs.

Weakness: Less precise. You might get creative matches that don’t quite fit. A property described as “great for entertaining” might have a large deck but a tiny kitchen, depending on the listing agent’s emphasis.

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When to Use Each Method

Use Filters When You Have Hard Requirements

Best for:

• Budget constraints: “Client can’t go above $400k”
• Specific room counts: “Exactly 4 bedrooms, no more (can’t afford utilities), no less (needs space)”
• Location boundaries: “Must be in Jerome School District for kids”
• Property type: “Only single-family homes, no condos or townhouses”

Why it works: Filters guarantee boundaries. If your client genuinely can’t afford more than $400k, semantic search might suggest a “perfect” $425k home that doesn’t help anyone. Filters keep you honest.

Example scenario:

Client: “We love everything about this house, but we can’t go above $375k because of our loan approval.”

You: Use filters to set max price $375k, ensuring you never waste their time on out-of-reach properties.

Use Semantic Search for Lifestyle Matching

Best for:

• Describing a feeling: “Cozy cottage vibe,” “Modern industrial loft,” “Warm traditional farmhouse”
• Feature combinations: “Open concept for entertaining,” “Split bedroom floor plan for privacy”
• Flexible preferences: “Updated within last 10 years, doesn’t have to be brand new”
• Client descriptions: “Something with character and charm”

Why it works: Semantic search finds listings that feel right based on descriptions, even if they don’t tick every box. A client saying “great for entertaining” might love either a home with a huge deck OR one with an open kitchen—they don’t need both.

Example scenario:

Client: “We want something modern but not too cold or sterile. Warm modern, you know?”

You: Semantic query “warm modern with natural light and wood accents” finds properties with that aesthetic, even if listings use phrases like “contemporary with rustic touches.”

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The Hybrid Approach (Recommended for Most Searches)

Most agents get the best results combining both methods. Use filters to enforce boundaries, semantic to prioritize matches.

How to Combine Effectively

1. Set filters for non-negotiables:

   • Price: $300k - $500k (client’s budget range)
   • Beds: 3+ (minimum required)
   • City: Twin Falls (work location constraint)
   • Property type: Single-family (lifestyle preference)

2. Use semantic query for preferences:

   • “Updated kitchen with granite countertops and stainless appliances”
   • “Large backyard great for dogs and kids”
   • “Mountain or valley views”

Results will match ALL the filter criteria (price, beds, city, type) and be ranked by how well they match the semantic description. Properties with spectacular views and granite counters float to the top; those without those features appear lower in the list but still within your client’s budget and location.

Real-World Example

Client brief:

“We need 3-4 bedrooms for our growing family, must be under $500k, and want something move-in ready with a modern kitchen. Bonus if there’s RV parking for my husband’s boat.”

Search strategy:

Filters:
  - Price: $0 - $500,000
  - Beds: 3-4
  - City: (client's preferred area)

Semantic:
  - "Move-in ready with updated modern kitchen and RV parking"

Result: You get homes in their price range and bedroom count, sorted by best matches for “move-in ready,” “modern kitchen,” and “RV parking.” Homes with all three rank highest; homes with just one or two rank lower but still appear if they fit the hard requirements.

Pro Tip

When using the hybrid approach, think of filters as “must-haves” and semantic as “nice-to-haves.” Filters narrow the universe; semantic ranks what’s left.

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Common Mistakes to Avoid

Mistake 1: Putting Numbers in Semantic Search

Don’t do this:

Semantic: "3 bedrooms under $400k in Twin Falls"

Why: Semantic search isn’t optimized for exact numbers. Use filters for specs, semantic for characteristics.

Do this instead:

Filters: 3 beds, $0-$400k, Twin Falls
Semantic: "Open floor plan with updated fixtures"

Mistake 2: Being Too Vague with Semantic

Weak semantic query:

"Nice house"

Why: Too generic. Every listing agent describes their property as “nice”—you won’t get meaningful ranking.

Better semantic query:

"Spacious entertaining areas with natural light and outdoor patio"

Why: Specific features that not all homes have. Creates clear differentiation in results.

Mistake 3: Forgetting Clients Don’t Think in Database Fields

Agent mindset (database-oriented):

“I need to search BedroomsTotal ≥ 3 AND HasPool = true AND YearBuilt > 2010…”

Client mindset (lifestyle-oriented):

“I want a newer home with a pool where we can host summer BBQs with family.”

Better approach: Translate client language into hybrid search:

Filters: 3+ beds, Built after 2010, Has pool
Semantic: "Great for hosting family gatherings and summer entertaining"

This finds homes matching the specs but ranks those with large patios, outdoor kitchens, and open layouts higher—capturing the spirit of what the client wants.

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Common Misconceptions

”Semantic search replaces filters”

Not true. They serve different needs. Filters enforce hard constraints; semantic captures soft preferences. Use both together.

”Semantic search is slower”

False. Both run in milliseconds. Semantic requires one extra step (embedding generation) but it’s imperceptible to users. The time difference is <100ms, which humans can’t detect.

”I need to learn special syntax for semantic”

Nope. Just describe what you want in plain English. Avoid real estate jargon if possible—write like you’re texting a friend about your dream home. The AI handles the rest.

”Filters are old-school and semantic is better”

Wrong mindset. Filters are essential for boundaries. Semantic is essential for nuance. Neither is “better”—they solve different problems. The best agents use both strategically.

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When to Use Search Methods by Situation

First-Time Homebuyers

Challenge: They often don’t know what they want or use vague descriptions like “something nice and affordable.”

Approach:

1. Start with filters for budget (usually a hard constraint)
2. Use broad semantic queries to show variety: “Family-friendly starter homes” vs. “Modern minimalist condos”
3. Watch which results they engage with, then refine semantic queries based on patterns

Example dialogue:

Buyer: “We want something affordable where we can start a family.”

Agent: “Let’s start with homes under $350k in good school districts. I’ll show you some family-friendly options with different styles—let me know which vibes resonate.”

Uses filters for budget/schools, semantic for “family-friendly with room to grow”

Luxury Buyers

Challenge: Price is less constrained; aesthetic and lifestyle fit are paramount.

Approach:

1. Use light filters (maybe just location and property type)
2. Rely heavily on semantic for specific aesthetic: “Contemporary mountain retreat with floor-to-ceiling windows” or “Classic estate with formal entertaining spaces”
3. Lean on vision-based search if they show you inspiration photos

Relocating Professionals

Challenge: Don’t know the area; focused on commute, amenities, and move-in readiness.

Approach:

1. Filters for location (near office or transit), price, beds
2. Semantic for lifestyle: “Low-maintenance modern home near restaurants and shops” or “Quiet neighborhood with home office space”
3. Emphasize neighborhood context in results (schools, commute times)

Investors

Challenge: Focus on numbers (ROI, rent potential, price per sqft) over aesthetics.

Approach:

1. Heavy use of filters: price, property type, year built, square footage
2. Light semantic: “Income-producing duplex” or “Value-add fixer-upper potential”
3. Use custom searches and alerts for new listings matching their buy box

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Related Documentation

Want to learn how to use these features?

• Use Semantic Search — Step-by-step guide
• Combine Filters with Semantic Queries — Advanced techniques

Need quick reference?

• Search API Reference — Technical endpoint details

Curious about the technology?

• How Semantic Search Works — Vector embeddings and AI matching

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The Bottom Line

Use filters to set boundaries (what the client can’t compromise on). Use semantic search to capture preferences (what they want but could flex on). Combine both for the best results, and don’t overthink it—experiment and see what works for each client!