Technical guide · July 2026

HomeAndConstructionBusiness Schema: The Complete Guide for Remodelers

Structured data is how search engines and AI systems read your business — type, service area, contact details, proof. Most remodeling companies either have none or have broken markup, and both states forfeit the signal. This guide covers the exact JSON-LD pattern a design-build or remodeling firm should ship, the traps that silently split your business into two entities, and how to validate it.

In our June 2026 audit of 40 luxury design-build firms, 45% had no LocalBusiness-family structured data at all. If you hand this guide to your web person, they can close that gap in an afternoon.

Why the generic LocalBusiness type costs remodelers citations

Schema.org has 50+ subtypes of LocalBusiness. Search and answer engines use the subtype to filter category queries — “best design-build firm in Austin” matches businesses typed as construction businesses, not generic local businesses.

For remodelers, design-build firms, and custom builders, the correct type is HomeAndConstructionBusiness — a purpose-built subtype for construction and home-services companies. Using the parent LocalBusiness when this subtype applies throws away category-specific signals. Declaring both HomeAndConstructionBusiness and Organization in a type array covers the business-entity and knowledge-graph cases at once.

The full @graph pattern

Amateur implementations scatter isolated schema blocks across pages — an Organization here, a LocalBusiness there — that never reference each other. The professional pattern is a single @graph: multiple entities linked by @id, so crawlers understand your place, your organization, and your services as one coherent business.

Homepage JSON-LD — full @graph for a design-build firm
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Place",
      "@id": "https://yourfirm.com/#place",
      "geo": {
        "@type": "GeoCoordinates",
        "latitude": "30.2672",
        "longitude": "-97.7431"
      },
      "address": {
        "@type": "PostalAddress",
        "streetAddress": "1234 Ranch Road",
        "addressLocality": "Austin",
        "addressRegion": "TX",
        "postalCode": "78746",
        "addressCountry": "US"
      }
    },
    {
      "@type": ["HomeAndConstructionBusiness", "Organization"],
      "@id": "https://yourfirm.com/#organization",
      "name": "Your Firm Name",
      "url": "https://yourfirm.com/",
      "telephone": "+15125551234",
      "logo": "https://yourfirm.com/logo.png",
      "image": "https://yourfirm.com/featured-project.jpg",
      "location": { "@id": "https://yourfirm.com/#place" },
      "areaServed": [
        { "@type": "City", "name": "Austin" },
        { "@type": "City", "name": "Westlake Hills" },
        { "@type": "City", "name": "Lakeway" }
      ],
      "sameAs": [
        "https://www.houzz.com/pro/yourfirm",
        "https://www.instagram.com/yourfirm/",
        "https://www.linkedin.com/company/yourfirm/"
      ]
    }
  ]
}

Two rules make or break this:

  • One @id per entity, sitewide. Every page that references your business must use the same @id (https://yourfirm.com/#organization). If your contact page emits a different one, crawlers see two separate half-strength businesses.
  • JSON-LD, not microdata. A single <script type="application/ld+json"> block, in the head or body. It’s the format Google explicitly prefers and the easiest for AI crawlers to parse.

Service pages: the Service type

Each major service page — whole-home renovation, additions, kitchens — should carry a Service block that points back to your organization via provider. This is what lets a query like “whole-home remodel Scottsdale” resolve to your firm specifically.

Service page JSON-LD
{
  "@context": "https://schema.org",
  "@type": "Service",
  "name": "Whole-Home Renovation",
  "serviceType": "Home renovation",
  "provider": { "@id": "https://yourfirm.com/#organization" },
  "areaServed": { "@type": "City", "name": "Austin" },
  "description": "Design-build whole-home renovation for
    properties in Westlake, Barton Creek, and Lake Travis —
    typical projects $400K and up."
}

NAP alignment: your schema vs your Google Business Profile

NAP — name, address, phone — must be character-identical across your structured data, your site footer, your contact page, and your Google Business Profile. “Smith Design Build, LLC” in schema and “Smith Design-Build” on Google are two different entities to a matching algorithm.

The audit sequence we run:

  1. Pull the exact name, address, and phone from your Google Business Profile.
  2. Grep your site for every occurrence of your phone number and address — footer, contact page, schema.
  3. Normalize everything to the GBP version (or fix GBP if it’s wrong — but pick one canonical form).
  4. Use E.164 phone format in schema (+15125551234) even when the visible site shows (512) 555-1234.

sameAs: wiring your profiles into one entity

The sameAs array tells crawlers that your Houzz profile, Instagram, LinkedIn, and directory listings are the same business as your domain. For luxury remodelers this matters more than most industries — your strongest proof often lives on Houzz, and sameAs is how that authority transfers to the site you own. List only profiles you control and keep active.

Common traps

Trap What it does
Different @id per page Splits your business into multiple weak entities — the #1 error we find
Generic LocalBusiness type Loses construction-category filtering in “best [type] in [city]” queries
Hours as a text string openingHoursSpecification must be structured objects, not “Mon-Fri 8-5”
Schema NAP ≠ Google profile NAP Conflicting signals reduce citation confidence in AI answers
Ratings in schema with no visible reviews Rich-result eligibility loss; potential manual action
Plugin schema islands Multiple disconnected blocks that never link via @id — noise, not a graph

How to validate

  1. Google Rich Results Test (search.google.com/test/rich-results) — paste your homepage URL; confirm the business entity is detected with zero errors.
  2. Schema.org validator (validator.schema.org) — catches structural issues Google’s tool tolerates.
  3. Re-test after every site update. Theme and plugin updates silently break markup; a quarterly check takes five minutes.
The payoff: valid structured data makes a site eligible for rich results and gets it cited in AI-generated answers at roughly 1.3–1.5× the rate of an identical site without it. For firms competing on two-or-three-name shortlists, that multiplier is the game.

For the strategy layer above the markup — why entity signals now decide luxury shortlists — see our answer engine optimization guide.

Don’t want to hand-write JSON-LD? Entity Foundation ships the full @graph, NAP audit, Google profile alignment, and validation in one $899 engagement — with a before/after scorecard showing exactly what changed.

Methodology: Structured-data gap figures from the Renovique homepage audit, June 2026 (40 luxury design-build firms, 14 metros, anonymized). Citation-rate multiplier from published 2026 structured-data research. Code examples validate against Schema.org and Google’s Rich Results Test as of publication.