Executive Summary

Finding retail shop leases is time-consuming and error-prone for landlords, brokers and multi-store retailers: site discovery is fragmented across listings, zoning data, footfall measurements and subjective broker knowledge, and a single wrong lease can easily cost an operator $100k+ in lost margin over a few years. Across roughly 180,000 commercial retail stakeholders the market spends on the order of $18.0B annually on site-selection, data and services, which reflects both the scale and the high-stakes nature of these decisions. You could build an AI-powered site search and storefront suitability scoring platform that ingests satellite and street imagery, anonymized mobility and transaction feeds, points-of-interest, and lease terms, then outputs expected revenue per square foot, a probability of meeting KPI targets, and a ranked shortlist for leasing teams. Deliver it as a $100K ACV enterprise SaaS with optional white-glove analytics and CRM integrations, plus a human-in-the-loop review to surface local intelligence and validate model outputs. The timing is favorable: recent advances in models that synthesize imagery, footfall and POI data materially improve storefront-level predictions, anonymized mobility and transaction datasets are more accessible, and retailers are increasingly optimizing portfolios rather than expanding indiscriminately. Given a Market Score of 90/100 and Revenue Potential of 88/100, the $18.0B addressable spend and the industry’s appetite for profitable-location insights indicate real willingness to pay for demonstrable ROI. To stand out you will need proprietary transaction linkages or exclusive mobility feeds, explainable and auditable suitability scores, tight integrations with broker/landlord workflows, and a go-to-market concentrated on flagship retail chains and large landlords—while being honest about challenges around data licensing costs, sparse ground-truth labels in some markets and long enterprise sales cycles; if those are managed, the product can produce measurable cost avoidance that justifies the $100K ACV.

Market Opportunity

Finding retail shop leases is hard — AI site-search + suitability scoring targets a $18.0B = 180,000 commercial retail stakeholders (landlords, brokers, multi-store retailers) x $100K ACV for site-selection + data + services total addressable market with medium saturation and a year-over-year growth rate of 12% CAGR for proptech and location-intelligence segments; higher (15-25%) for AI-enabled offerings.

Key trends driving demand: AI-enabled location analytics -- Models now synthesize imagery, footfall and POI data to predict revenue by storefront; Brick-and-mortar optimization -- Retailers prioritize profitable stores and experience venues, increasing demand for better site decisions; Data accessibility -- Growing availability of anonymized mobility and transaction data lowers cost of building predictive models; Lease digitization -- Landlords/brokers adopting digital listing and e-signing workflows creates integration points.

Key competitors include CoStar Group / LoopNet, Crexi, Reonomy, Placer.ai, Buxton.

Sign in to access

Finding retail shop leases is hard — AI site-search + suitability scoring

Executive Summary

Market Validation

Market Opportunity

More in Real Estate Tech

High COI fees for property managers — lightweight automated tracking (SaaS)

Stop losing deals to missed follow-ups → AI follow-up CRM for agents

End field chaos: replace trailer offices with mobile-first iPad site ops

Hard-to-show spaces — instant 3D tours from a phone video

Construction productivity gap — AI agents automating field workflows

Pakistan property friction: AI CRM+ERP & automation to streamline deals