TL;DR
- We track 55 questions covering land sourcing, planning, site risk, development feasibility and AI masterplanning.
- The figures below draw on 469 answers collected from ChatGPT, Gemini and Google AI Overview on 18 August 2026.
- LandTech leads on visibility at 23.45%, followed by Searchland at 18.55% and TestFit at 16.63%.
- Autodesk Forma and Nimbus complete the leading five, with visibility of 13.43% and 12.37%.
- Official planning sources set the factual frame, while focused product pages and comparison guides decide which platforms make the shortlist.
Land intelligence software now covers far more than a map with ownership and planning overlays. Property developers and investors use these platforms to find sites, read zoning rules, test buildability, model returns, compare risk and produce an investment case before they commit capital.
AI assistants have become part of that research process. A developer can ask for the best tool to source land in Europe, calculate residual land value, explain a Portuguese planning document or create several site concepts from one parcel. Each question can produce a different shortlist.
This study measures which platforms make those shortlists, how much of the answer each one captures and which sources support the recommendation.
How was the 2026 land intelligence study measured?
We track a fixed set of 55 buyer questions in the United Kingdom. The questions cover five practical areas:
- land sourcing and opportunity discovery
- planning, zoning and buildability
- development feasibility and investment returns
- site risk and due diligence
- AI masterplanning and property intelligence
The figures in this report draw from 469 answers collected on 18 August 2026 across ChatGPT, Gemini and Google AI Overview. We record what each engine returns, not what a vendor says about itself.
Visibility is the share of answers that name a platform. Share of voice is the platform's share of all measured brand mentions. Average position shows where a platform ranks when it appears, with a lower number being better. Sentiment is scored out of 100 and reflects how positively the engines describe the platform.
No single metric tells the whole story. A platform can appear often but late in the list. Another can appear rarely but rank first when named. The useful reading combines reach, answer share and position.
Which AI land intelligence platforms lead in 2026?
LandTech leads the measured category on both visibility and share of voice. It appears in 23.45% of answers and takes 27.82% of the measured answer space. Searchland follows with 18.55% visibility, while TestFit reaches 16.63%.
- LandTech: 23.45% visibility, 27.82% share of voice, 2.4 average position, 59 / 100 sentiment.
- Searchland: 18.55% visibility, 15.28% share of voice, 2.1 average position, 56 / 100 sentiment.
- TestFit: 16.63% visibility, 15.13% share of voice, 2.4 average position, 59 / 100 sentiment.
- Autodesk Forma: 13.43% visibility, 14.10% share of voice, 5.0 average position, 60 / 100 sentiment.
- Nimbus: 12.37% visibility, 9.15% share of voice, 3.3 average position, 58 / 100 sentiment.
- Aprao: 6.18% visibility, 6.94% share of voice, 4.3 average position, 59 / 100 sentiment.
- Deepblocks: 5.76% visibility, 2.88% share of voice, 3.8 average position, 57 / 100 sentiment.
- Landstack: 4.48% visibility, 1.92% share of voice, 3.8 average position, 54 / 100 sentiment.
- LandHawk: 4.05% visibility, 2.14% share of voice, 3.4 average position, 56 / 100 sentiment.
- LandLens: 3.20% visibility, 1.77% share of voice, 3.8 average position, 54 / 100 sentiment.
- REalyse: 1.71% visibility, 1.11% share of voice, 3.9 average position, 54 / 100 sentiment.
- GoSiteHunt: 1.28% visibility, 0.96% share of voice, 4.8 average position, 56 / 100 sentiment.
- LiteHaus: 0.85% visibility, 0.81% share of voice, 2.3 average position, 56 / 100 sentiment.
The top three lead for different reasons. LandTech has the widest spread across sourcing, planning, feasibility and due diligence. Searchland is strongest in land sourcing and UK planning workflows. TestFit dominates questions about site layouts, generative design and connecting plans to financial models.
Autodesk Forma also performs well on planning and site-design questions, but its 5.0 average position shows that it often appears deeper in the answer. Nimbus has less answer share than the leading four, yet its broad set of pages for developers, planners and constraints keeps it visible across the buyer journey.
At the other end of the table, LiteHaus has a useful early signal. It averages 2.3rd place when named. The limit is reach rather than rank.
Why does LandTech lead AI recommendations?
LandTech gives engines several focused pages to retrieve. Its homepage explains the category, while dedicated pages cover LandInsight, land search, site finding, planning data and residual valuation.
The LandInsight product page received 46 citations in the measured source set. Its land-search page and site-finding pages also appear across sourcing and assessment questions. That page depth helps an engine connect one company to several buyer jobs without relying on a single broad claim.
Searchland follows a similar pattern. Its homepage earned 74 citations, the most of any vendor homepage in the measured set. Separate pages cover planning constraints, property appraisal, commercial property and architect workflows. These pages use the terms buyers ask about and explain the task before the product detail.
TestFit wins a narrower but important part of the market. Its homepage received 32 citations and its Site Solver page received another 11. When the question asks for parcel-based layouts, fast feasibility or a site plan linked to a pro forma, TestFit is often the clearest answer.
Which questions split the category?
Broad platform questions favour vendors with several connected capabilities. For the question "What software combines land sourcing, planning, finance and masterplanning?", LandTech appeared in every measured answer. Searchland appeared in two thirds, followed by TestFit, Nimbus and Aprao.
Planning-document questions produce a different result. When asked which AI tools can read municipal planning documents and explain buildability, TestFit appeared in 77.8% of answers. Autodesk Forma and Deepblocks each appeared in 33.3%.
Land-deal discovery questions shift the list again. For tools that identify undervalued plots using price history and market signals, Searchland led, followed by LandTech and Nimbus.
This is why a single "best land software" query gives a weak view of the market. Buyers move between five distinct jobs, and each job pulls a different mix of product pages, public records and third-party guides.
Where do AI assistants get land and planning evidence?
Public sources provide the factual base. GOV.UK was retrieved in 19.83% of measured answers and received 249 citations. Planning Data, the Planning Portal, RICS, the British Geological Survey, local authorities and Portuguese government sources also appear throughout the set.
These sources answer questions that a software vendor cannot settle with marketing copy. They define planning policy, flood-risk checks, land records, valuation methods and the status of official data.
Vendor sites then translate those facts into a workflow. land.tech received 178 citations, searchland.co.uk received 155, nimbusmaps.co.uk received 82 and testfit.io received 72. YouTube received 112 citations across the category, which shows the value of a visual explanation for site planning and buildability.
The strongest pages make their evidence chain clear. A planning claim links to the authority or dataset. A feasibility claim shows the inputs and assumptions. A product page states the job it performs, who it is for and what the output contains.
Which third-party pages shape the shortlist?
Comparison and guide pages carry a large amount of the commercial research.
GoSiteHunt's "Best Property Development Feasibility Software 2026" received 23 citations. August's guide to UK property sourcing software received 19. Felt's property-mapping guide received 17. Other cited pages cover AI tools for real estate developers, early-stage feasibility software and property-development appraisal.
These pages matter because they sit between a buyer question and a vendor recommendation. They define the selection criteria, decide which names belong in the set and give the engine a neutral-looking source to support the answer.
Inclusion alone is not enough. A platform needs a clear description, an up-to-date product page and evidence that supports the exact use case. A weak or stale profile can earn a mention without earning a recommendation.
Are paid placements already present in property-development answers?
Yes. ChatGPT displayed ads in 83 of the 469 measured answers, a 17.7% rate across the full set. Ads appeared on 48 of the 55 tracked questions, with 39 advertisers in market.
The leading advertisers were not the same as the leading land-intelligence platforms. GetAgent held 20% of the measured ad share, followed by Unloq at 10%, Monday.com at 9%, Bradfords Building Supplies at 7% and Jotform at 7%.
That split matters. The organic answer and the paid placement solve different problems. A land-intelligence platform may lead the evidence-based shortlist while an adjacent property or software brand buys the placement around it.
What should a developer check before choosing a platform?
A useful land-intelligence platform should be able to show:
- which countries and planning systems it supports
- which official cadastral, zoning and environmental sources it uses
- when each source was last updated
- how it handles conflicting or missing records
- how buildable area, density and unit count are calculated
- whether users can change assumptions and compare scenarios
- how site plans connect to costs, values, returns and risk
- whether the output links each claim back to its source
- how the platform works for on-market and off-market opportunities
- what can be exported for an investment committee, planner or architect
The right choice depends on the job. A UK land-sourcing team may value planning constraints and off-market coverage most. An architect may care about fast site layouts. An investor entering Portugal may need source-backed zoning, infrastructure, market evidence and a clear record of uncertainty.
What will change the 2026 leaderboard?
The category is still open outside the leading three. Even LandTech appears in fewer than one quarter of measured answers, and several buyer questions have no clear platform winner.
Three moves are likely to change the rankings:
- More focused product pages. One page for land sourcing, one for zoning verification, one for feasibility and one for investment reporting gives engines a clearer match.
- Stronger evidence links. Platforms that show the exact public source behind a planning or risk claim will be easier to trust and cite.
- More independent comparison. Editorial guides, creator videos and current practitioner discussions give engines a third-party reason to include a platform.
The leading brands already do parts of this well. The next shift will come from platforms that connect the whole workflow without reducing it to one vague AI property claim.
If you want to measure the buyer questions in your category, book a 20-minute call with Marco: https://calendly.com/marco-ai-heroes/20min.
