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

AI is fast becoming a practical advantage for Peru's real estate sector in 2025: global market forecasts show AI in real estate jumping from roughly $222–303B in 2024–25 and growing at ~34% CAGR toward nearly $975–989B by 2029, which means tools like automated valuation, predictive maintenance and smart leasing are no longer niche experiments but scalable levers for efficiency (Global AI in Real Estate Market Report 2024–2029).

Locally, Peru's market felt inflationary shocks in 2022–23 but reports note stabilization and renewed leasing activity in Lima - making AI-driven pricing and lease automation timely for operators trying to cut costs and manage risk (Peru real estate credit analysis - Martini.AI report).

For agents and managers ready to act, practical training like Nucamp's Nucamp AI Essentials for Work bootcamp - 15-week AI training for the workplace teaches prompt-writing and AI tools that turn forecasts into faster deals and lower operating costs.

Peru's Ley N° 31814 (published 5 July 2023) moves AI from theory to regulated practice by using a risk-based approach that both encourages adoption and sets firm guardrails: prohibited “unacceptable” uses (like certain social scoring and some forms of real‑time biometric ID), a high‑risk band for credit scoring, employment screening and critical‑infrastructure tools, and lighter obligations for chatbots and general‑purpose models; the law also centralizes oversight in the Presidency of the Council of Ministers via the Secretariat of Government and Digital Transformation and layers in rules on transparency, human oversight, data minimization, cross‑border data controls and incident reporting (see the Nemko overview of Peru AI Law 31814 lifecycle requirements and the OECD summary of the Peru AI initiative).

For beginners in real estate: treat automated pricing, credit assessment and tenant‑screening tools as potentially high risk, document models and data sources, build simple human‑in‑the‑loop checks, and plan for periodic audits and clear consent language in Spanish - these steps reduce compliance risk and make AI a practical, trustworthy partner rather than a regulatory headache; think of classifying an automated credit check as a red flag in a loan pipeline - if it's high risk, it needs extra controls before it moves forward.

For a closer read on draft extensions and enforcement mechanics, review the OECD analysis of draft extensions and enforcement mechanics.

“In 2024, US lawmakers introduced more than 700 AI-related bills, and 2025 got off to an even faster start, with more than 40 proposals on the books in the first few days of the new year.”

Peru's 2025 rebound - stable rents around a 6% gross yield and renewed sales activity in Lima and fast‑growing secondary cities - creates fertile ground for practical AI tools: local automated valuation models (AVMs) and price‑optimization engines tuned to district-level dynamics and inflation‑adjusted trends can sharpen listings and underwriting (see the market snapshot from Peru real estate price history (Global Property Guide)); demand‑heatmaps and site‑selection models that factor proximity to new metro lines (properties within ~1km of stations often see 15–20% uplift) help investors spot pockets of outsized appreciation (Peru price forecasts (The LatInvestor)); Spanish‑language lease‑abstraction and contract‑search AI cuts legal bottlenecks and speeds closings; construction‑management models that flag delays from Sierra seasonal weather and logistics reduce overrun risk; and tenant chatbots plus automated lead triage turn mounting buyer interest into faster leases and sales - together these use cases convert macro recovery signals into day‑to‑day competitive advantage, whether a broker in Miraflores or a developer in Arequipa.

For hands‑on examples and tuned prompts for Spanish contracts and construction forecasting, see practical guides from local bootcamps and proptech pilots (Top AI prompts and use cases for real estate in Peru (local bootcamps and proptech pilots)), which help bridge data to decisions with simple human‑in‑the‑loop checks that keep models useful and auditable.

“This sales record isn't random. It's the result of both structural and market-specific factors - especially project locations and a shift toward units averaging 65 square meters, reflecting higher demand for compact, efficient living spaces.”

Peruvian real‑estate teams adopting AI must treat privacy and compliance as front‑line business tools: Ley N° 31814 creates a risk‑based regime that puts automated credit scoring, tenant screening and other high‑impact systems into a stricter band, mandates human oversight and transparency, and ties data duties to existing personal‑data rules (Law No.

29733) under the ANPDP - so every model that touches PII needs documented purpose, minimised inputs and clear consent in Spanish (Nemko overview of Peru's AI regulation).

The Regulations approved by Supreme Decree No. 115‑2025‑PCM (9 Sept 2025) formalise institutional roles (PCM‑SGTD, CNIDIA), require traceability and retention of impact documentation (three years minimum for assessments), and set staged compliance deadlines by sector with an entry‑into‑force window starting ~Jan 2026; for practical operations that means mapping each AI use to a risk tier, enforcing human‑in‑the‑loop checks on high‑risk flows, pseudonymizing training data where possible, and building vendor controls and incident‑reporting paths (including the SGTD's citizen channel at gob.pe/iaperu) before tools hit production (Lexology summary of the approved regulations).

A simple operational rule: treat model decisions that affect tenancy, credit or hiring like a physical tenant folder - date, consent form and audit trail - so teams can respond quickly to access, rectification or erasure requests and to regulatory inspections.

Classifying risk and designing controls for high‑risk AI in Peru starts with the clear three‑band framework found in the Regulations - unacceptable (prohibited), high risk (credit scoring, employment decisions, infrastructure, etc.), and acceptable/limited uses - and translates directly into practical steps for real‑estate teams: first, inventory every AI touchpoint (AVMs, tenant screening, lease‑automation, chatbots) and map each to a risk tier using the criteria in the approved Regulations (Supreme Decree No.

115‑2025‑PCM) so credit‑related or tenant‑screening tools land in the high‑risk bucket (Analysis of Supreme Decree No. 115‑2025‑PCM - Peru AI regulations (Lexology)); second, apply mandatory design controls for high‑risk systems - document data sources, run impact assessments where appropriate, keep traceability and impact documentation (minimum three years) and install human‑in‑the‑loop checkpoints and stop‑gates so a trained reviewer can halt or correct automated decisions (Nemko overview of Peru's AI risk‑based regulatory regime).

Operationally, that means pseudonymize training data when possible, log model outputs like a tenant folder for audits, build vendor due diligence and an incident workflow tied to the SGTD/PCM channel, and align timelines to the staged compliance windows (entry into force ~Jan 2026 for many private‑sector obligations).

For guidance on classifying borderline systems and pre‑market verification, review the draft bill's four‑bucket approach and certification expectations (Access Partnership summary of Peru AI bill risk buckets and verification expectations); in practice, treat a tenant‑screening model like a stamped loan file - if it's high risk it needs controls before going live.

Start small, measure relentlessly, scale with governance: a practical roadmap for Peruvian real‑estate teams begins by choosing one high‑value pilot (for example, Spanish lease‑abstraction or a construction‑delay flagging pilot taught in local bootcamps) and explicitly mapping it to business KPIs like payback period, ROI, tenant turnover and average rent so success is unambiguous - see the Top 22 real estate KPIs for concrete metrics and formulas (Top 22 Real Estate KPIs and Metrics for 2025 Reporting).

Run the pilot with tight cost controls and phased staffing, quantify efficiency and accuracy gains, then apply AI‑project KPIs (efficiency, error rates, new leads, contribution to sales) so ROI is measurable at each stage; RSM's cost‑optimization advice and MIT Sloan's playbook on “smart KPIs” both recommend combining descriptive, predictive and prescriptive indicators so dashboards become action engines rather than passive scorecards (MIT Sloan Review: Enhancing KPIs With AI for Strategic Measurement).

Capture a vivid quick win - turn a manual lease folder into searchable contract terms in minutes with a Spanish‑tuned extractor - to build momentum, then harden controls: data quality checks, human‑in‑the‑loop reviews, vendor due diligence and a documented incident workflow before scaling across portfolios (Lease Abstraction Tools Tuned for Spanish Leases).

With that cycle - pilot, measure, govern, scale - AI becomes a measurable business capability, not an experiment.

“what gets measured gets managed.”

Agents and brokers in Peru can score fast, practical wins by combining a few low‑cost AI tools: use Clipboard AI to pull names, numbers and clause text out of scans or screenshots so spreadsheets and CRMs populate automatically (the Community edition is free and handles Latin‑alphabet Spanish), deploy Spanish‑tuned lease‑abstraction tools to turn a manual lease folder into searchable contract terms in minutes and slash legal bottlenecks, and add a lightweight chatbot or 24/7 lead‑triage bot to capture and qualify prospects after hours so hot leads don't cool off overnight; together these moves turn hours of repetitive work into same‑day actions that speed closings, reduce errors and let brokers spend more time selling and less time copy‑pasting (Clipboard AI copy‑paste automation for Latin‑alphabet Spanish, Spanish lease‑abstraction tools for Peruvian leases, Copy.ai chatbot and AI writing tools).

Building a practical Peru‑ready AI stack means layering clean, local data with proven model types and lightweight, API‑first vendors: start by fusing AVM feeds, MLS-like listings and land‑parcel maps so models can capture Lima's micro‑neighborhood shifts (The Warren Group's blueprint shows how AVM+MLS+parcel fusion yields faster, more accurate, portfolio‑scale valuations); add computer‑vision layers that read photos to surface condition and renovation signals - Restb.ai's photo‑based condition models are a clear match for markets where public data miss property quality - and orchestrate NLP extractors for Spanish lease abstraction and contract search (see the practical application map in MindInventory's AI guide).

Favor cloud/SaaS vendors with real‑time APIs and entity‑resolution support to avoid brittle integrations, insist on modular delivery (AVM API, image‑tagging, NLP extractor), and require traceability and exportable logs for compliance.

A useful rule: pilot with one valuation or lease‑abstraction flow, aim for a measurable quick win (like converting a 100‑folder lease backlog into searchable clauses overnight), then harden data cleansing, entity resolution and vendor SLAs before scaling across portfolios.

Automated batch valuations in minutes, not days.

When deploying AI in Peru's real‑estate workflows, the most urgent risks are not hypothetical: tenant‑screening and credit models trained on flawed records can reproduce and amplify social bias, create opaque “black boxes” for leasing decisions, and trigger costly legal exposure - U.S. lawsuits (and a $2.275M settlement in the SafeRent case) show how quickly automated screening can lead to claims of discrimination (SafeRent tenant-screening lawsuit and $2.275M settlement details), while reporting by advocates and researchers documents how scores can be built to “predict the likelihood of evictions and skipped payments” using tainted inputs (Mozilla Foundation report on AI bias in tenant screening) and how reliance on scores often hides racial disparities (NCLC report on tenant screening harms and racial disparities).

A vivid caution: one applicant with sixteen years of on‑time rent was reportedly denied because an algorithm ignored voucher payments, showing how real people pay the price for unchecked models.

Practical operational controls include mandatory pre‑deployment testing and red‑teaming, ongoing bias‑drift monitoring, intersectional and proxy‑variable analysis, meaningful human‑in‑the‑loop gates and transparent dispute channels, plus independent validation and documented remediation plans - steps advocated by testing and bias‑testing experts to avoid hidden harms and regulatory fallout (Guidance on rigorous AI bias testing for credit and tenant screening).

These safeguards turn AI from a liability into a manageable tool that protects tenants, reputations and balance sheets.

“Tenant screening scores and recommendations create a misleading veneer of objectivity while concealing underlying racial disparities.”

Conclusion & Next Steps for beginners in Peru: start by mapping every AI touchpoint to the risk bands in Peru's Law 31814 and use practical guides to translate rules into tasks - document purpose, minimise inputs, and add human‑in‑the‑loop checkpoints so model decisions are stamped with a date, consent and reviewer initials like a tenant folder ready for inspection; the Nemko overview of Peru's AI regulation is a concise reference for lifecycle duties and data governance (Nemko - AI regulation in Peru (overview)).

Keep an eye on policy evolution and national strategy milestones via the OECD Peru policy navigator so compliance calendars and sandbox opportunities stay current (OECD.AI - Peru policy navigator (national AI policy)).

For skills and practical pilots, invest in focused, workplace‑ready training - Nucamp's 15‑week AI Essentials for Work teaches prompt writing, tool selection and hands‑on prompts that turn a lease backlog into searchable contract clauses and a small AVM pilot into measurable ROI; register early to lock in the 15‑week curriculum and early‑bird pricing (Nucamp AI Essentials for Work - 15-week bootcamp (register)).

A simple operational checklist: 1) inventory and risk‑classify use cases, 2) run a small pilot (lease extraction or AVM), 3) document data sources and decisions, 4) enforce human review and dispute channels, and 5) train staff with a practical bootcamp so AI becomes a governed, revenue‑positive capability - not an untested liability.