The Complete Guide to Using AI in the Real Estate Industry in Kazakhstan in 2025

Kazakhstan's 2024–2029 AI push is directly material for real estate in 2025: a national AI platform, Smart Data Ukimet (93 connected databases) and a planned supercomputer mean faster property analytics, automated building‑permit checks and smarter utilities planning that feed Smart City and housing platforms - all big wins for developers, property managers and buyers.

Public projects like the National Spatial Data Infrastructure already put cartographic data online to power parcel mapping and site-selection tools, while e‑government and unified housing systems promise cleaner title records and faster transactions.

The transition comes with real caveats - cybersecurity, talent gaps and new laws - so phased pilots and audited models matter; for the policy framework see the government's AI development concept and reporting on Kazakhstan's digital reforms for context.

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“I have already spoken about accelerating the creation of a unified national digital ecosystem,” Tokayev said.

Kazakhstan's AI industry outlook for 2025 is upbeat but pragmatic: strong state direction, expanding infrastructure and a surge of startups are turning policy into pipelines and products.

The AI Development Concept (2024–2029) sets ambitious targets - large-scale reskilling, home‑grown models and $5 billion in AI exports by 2029 - and is already matched by concrete assets such as national AI hubs, the Alem.AI ecosystem and a nascent supercomputer cluster to support KazLLM and other local models; see Global CIO's review of “Key IT Trends in Kazakhstan 2025” for the policy and market numbers.

Growth is visible in Astana Hub's scale, new data‑center deals and mandatory AI curricula across universities that are rapidly feeding talent into industry; these moves help explain why Kazakhstan is being called Central Asia's AI powerhouse in industry coverage.

Still, practical limits remain - language gaps, cybersecurity rules and the need for trusted local cloud capacity will shape vendor choice and deployment timelines - so the immediate opportunity for real‑estate buyers, developers and proptech teams is to partner on targeted pilots that link sensor and spatial data to AI models hosted on domestic infrastructure while policy, skills and export ambitions continue to accelerate.

“The state must support such talent.”

With Kazakhstan's new national data layers and growing AI infrastructure, 2025 looks like the year real‑estate decision‑making goes from intuition to data‑driven: AI will speed mortgage approvals and contract checks, tighten fraud detection on title and payment instructions, and surface hyper‑local price signals so developers and investors can set dynamic pricing with more confidence.

Machine learning and agentic systems can turn parcel maps, building sensors and sales history into continuous valuations and vacancy forecasts, improving site‑selection and reducing costly guesswork; for practical uses see how AI refines transactions and appraisals in industry coverage and how agentic AI automates investment and asset optimisation.

Consumer search will get smarter too - recommendation engines that learn preferences can cut endless browsing into focused shortlists, while chatbots and virtual tours keep listings active 24/7.

The net result for Kazakhstan: faster, more transparent deals and lower operational costs if pilots are hosted on trusted local infrastructure and models are audited for bias and privacy.

Learn more about transaction automation and predictive valuation techniques in the Dotloop and Emitrr write‑ups linked below.

“They think we have created C-3PO [the anthropomorphic droid from Star Wars], when in reality we're just developing better ways to learn from data.”

AI is already moving from promise to practice in Kazakhstan's real‑estate workflows through well‑defined tools: Automated Valuation Models (AVMs) give instant, data‑driven price estimates that scale to thousands of listings and shrink traditional appraisal timelines from days or weeks to seconds, making bulk mortgage pre‑qualification and portfolio mark‑to‑market far more efficient; for a standards‑first treatment see ValuStrat's AVM review explaining why these systems should augment - not replace - expert valuers (ValuStrat review of Automated Valuation Models (AVMs)).

Elsewhere in the Kazakh market, AI is being applied to protect deals (AI‑powered fraud detection and identity verification) and to run focused, high‑ROI pilots that prove savings quickly before scaling across agencies and developers (AI-powered fraud detection and identity verification for Kazakh real estate and high‑ROI AI pilot programs for real estate in Kazakhstan).

Combined with Kazakhstan's growing national data layers, these applications - instant valuations, smarter listing recommendations and transaction screening - translate into faster closings, lower operational cost and clearer risk signals, provided models are transparent, audited and integrated with local professional judgement.

“It's a mathematical model - not an AI experiment - and while it delivers strong accuracy benchmarks, it's built to support valuers, not replace them.”

Kazakhstan's data backbone for real estate is shifting from patchwork to platform: the new national geodetic coordinate system QazTRF‑23, rolled out under the National Spatial Data Infrastructure project, establishes a network of continuously operating reference stations that tighten geodetic and cartographic accuracy and, in practice,

let planners and developers “pin” every parcel to a single coordinate frame - like giving Kazakhstan a GPS fingerprint for land (QazTRF‑23 national geodetic coordinate system launch news).

Service	Notes
Digital aerial photography	High‑resolution terrain imagery for mapping
Cartographic engineering	Creation of digital maps and GIS plans
Marine survey	Topographic maps of shelves and reservoirs
Geodetic survey	Engineering surveys using modern technical potential
Calibration of measuring instruments	Repairs and verification by qualified specialists
Updating information (RTK)	High‑precision satellite positioning corrections
Online store of thematic maps	Maps of natural and socio‑economic subjects
Spatial Data Web Portal	Collection of geodetic, topographic, cartographic and aerospace data

That precision is paired with operational services from the RSE National Centre of Geodesy and Spatial Information official website - digital aerial photography, cartographic engineering, RTK updates and a Spatial Data Web Portal - which feed maps, surveys and metadata into models used for site selection, utilities planning and AVMs; learn more about those services and the Spatial Data Infrastructure concept from the FGDC NSDI principles overview.

For real‑estate teams, the takeaway is simple: trusted, standardized spatial data and domestic reference frames reduce survey risk, speed permitting and make AI‑driven valuation and site‑selection far more reliable when pilots integrate these national layers early.

Regulation is moving from concept to concrete rules in 2025: the Mazhilis approved the Draft Law “On Artificial Intelligence” in its first reading on May 14, 2025, signalling a pivot from the 2015 Law “On Informatisation” toward a standalone AI framework that will reshape data, liability and public procurement for AI systems (analysis of Kazakhstan's Draft Law on Artificial Intelligence).

Core themes are human‑centred principles - legality, transparency, explainability and accountability - paired with a risk‑tiered approach that treats high‑risk public and critical‑infrastructure systems differently from lower‑risk tools, while imposing data‑protection limits such as explicit consent for biometric processing and tougher liability rules (including proposed criminal penalties for mass, automated misuse) as the country harmonises AI rules with existing personal‑data norms and the national AI platform governance (overview of Kazakhstan AI regulation and data protection).

Draft texts remain relatively compact (the initial bill is 28 articles) and experts note gaps - risk classification detail, stronger transparency mandates and enforcement capacity - so public‑sector procurement, platform hosting and pilots will need careful legal review and bias/privacy audits before scale to meet both the new law's safeguards and Kazakhstan's ambition to align with EU‑style, risk‑based norms (review of Kazakhstani draft AI laws and personal‑data provisions), making compliance a competitive advantage for real‑estate teams deploying AI.

“Kazakhstan is not pursuing a reckless race for progress, but is instead building a responsible system centered on human rights and social well-being.”

Designing privacy‑first, compliant AI for Kazakhstan real estate begins by using the law as a blueprint: limit collection to data that's strictly necessary, store personal records on servers in Kazakhstan, and secure explicit consent through the state or approved non‑state services rather than informal opt‑ins - steps grounded in the national Personal Data Law and its implementation rules (Adilet - Kazakhstan Personal Data Law: On Personal Data and Their Protection).

Create clear internal rules and appoint a named responsible person (the local equivalent of a DPO) to run audits, manage breach response and keep consent logs as required by regulators, and build automated checks so any incident is reported to the Ministry of Digital Development within one business day.

Treat biometric processing and AI systems affecting public administration or individual rights as high‑risk and design them for explainability and minimised retention to match the draft AI regime's risk tiers (Kazakhstan AI regulation overview).

Operationally, start with small, auditable pilots that use only depersonalised data or national platform services, document data flows, and bake in retention and deletion rules - a practical approach that turns legal constraints into competitive trust for developers, agents and buyers (DLA Piper - Kazakhstan personal data law summary).

“Only biometric data necessary for a specific purpose should be collected. Biometric information should not be stored longer than required.”

Start with a narrow, measurable pilot: pick a single use‑case (instant AVMs, fraud detection for title transfers or automated permit checks), inventory which national layers you need (NSDI cartography and the new QazTRF‑23 reference frame that lets planners “pin” every parcel to a single coordinate frame - like giving Kazakhstan a GPS fingerprint for land), and map where personal data will sit; lean on the new digital headquarters' coordination role to clear access to public data and infrastructure (Kazakhstan digital headquarters to accelerate AI integration).

Second, hard‑wire compliance before code: run a risk classification against the draft AI law, limit biometric use, and keep data residency and consent rules front‑and‑centre per national guidance (Kazakhstan draft AI law and risk-tiered compliance regulations).

Third, choose hosting and compute aligned with national platforms - QazTech and the coming supercomputer can shorten product cycles and reduce cross‑border data friction - then deploy models on depersonalised data and instrument logging and audits (QazTech supercomputer and platform acceleration in Kazakhstan).

Fourth, name a responsible lead (local DPO equivalent), define KPIs and rollback tests, and run a public pilot with transparent reporting to build trust. Finally, scale in phases: prove ROI, integrate more national layers, and bake legal audits, bias testing and incident playbooks into every release so compliance becomes a competitive advantage rather than an afterthought.

“It is not only about improving the legal framework for the functioning of AI. It is necessary to address matters of data fragmentation, the lack of clear regulations for the distribution of supercomputer capacity, cybersecurity, and the complete transition to the QazTech platform.”

Building an AI team in Kazakhstan means budgeting for rising local salaries, careful vendor choice and a hybrid hiring strategy: domestic talent pipelines (Tech Orda, AI‑SANA and university mandates) are expanding fast, but the market also pays - average ICT pay rose 54% to about 673,000 tenge in 2024 - so expect recruitment costs to be meaningfully higher than legacy IT budgets; for practical benchmarking see Global CIO's Key IT Trends in Kazakhstan 2025 for market and training figures.

Vendor selection should prioritise providers that can meet data‑residency and trusted‑software rules, local cloud and data‑centre options (Kazteleport, Kazakhtelecom) and national platforms like Alem.AI and the new supercomputer cluster that underpin Kazakh models and lower cross‑border friction; Astana Times coverage of Kazakhstan's digital push outlines how Alem.cloud, AlemLLM and new HPC capacity are changing procurement choices.

To fill immediate gaps, combine local hires with short-term foreign specialists using the Digital Nomad/Neo Nomad visa pilots, run narrowly scoped pilots with local vendors from Astana Hub to prove ROI, and budget for audited deployments and private‑cloud hosting to satisfy CIOCI and public procurement rules - one vivid reality: Kazakhstan now hosts a Central Asian supercomputer with NVIDIA H200‑class capacity, so partnering with domestic compute providers can cut months off model iteration time while keeping compliance risks manageable.

“For Kazakhstan, the development of AI is one of the top national priorities and is closely monitored by President Tokayev.”

Conclusion - next steps for beginners: start small, stay legal, and learn while you pilot; pick a single, measurable use case (an instant AVM or an AI‑powered fraud detection flow are ideal starters) and build a tight data map that respects Kazakhstan's emerging risk tiers and consent rules so compliance is not an afterthought but a competitive edge - see the concise overview of Kazakhstan's draft AI law and risk‑based approach for practical checkpoints (Nemko: AI regulation in Kazakhstan - risk tiers, data protection and compliance).

Run a short, auditable pilot that uses depersonalised data or explicit consent, host models on trusted domestic platforms where possible, and lean on local compute (the QazTech/supercomputer push shortens iteration time) to move from prototype to production faster.

Pair that pilot with focused upskilling so teams can write effective prompts, evaluate outputs and manage vendor risk - a practical course like Nucamp's AI Essentials for Work prepares non‑technical staff to run and govern workplace AI (Register for Nucamp AI Essentials for Work (15-week bootcamp)).

Finally, prove ROI quickly and document decisions: a high‑ROI pilot that saves time or flags risk turns regulatory friction into trust and gives you the momentum to scale confidently (Start high‑ROI real‑estate AI pilots in Kazakhstan).

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