Emlakjet and Endeksa Merge: A New Data-Driven Chapter for Property in Turkey

A merger that could reset how buyers and investors use real estate data

The Turkish real estate market is about to test a more integrated digital model. In a move that unifies marketplace reach and machine intelligence, Emlakjet and Endeksa have merged under the iLab umbrella, creating a single organisation that blends one of Turkey’s largest listing platforms with an AI valuation specialist. For anyone tracking property Turkey, this is not a subtle change: it promises to combine marketplace scale with automated valuation models and predictive analytics, and that combination matters for buyers, sellers, agents, and investors.

I’ll be candid: the merger looks like an obvious next step for proptech consolidation in Turkey, but integration is where value will be made or lost. Our analysis below looks at what the deal is, why it matters, how it could change property search and valuation, the risks involved, and what practical steps buyers and investors should take.

What happened and who’s in charge

• Emlakjet and Endeksa have merged under the same organisational structure within the iLab group.
• Emlakjet was founded in 2006 and is present in each of Turkey's 81 provinces.
• Endeksa is a data analytics and artificial intelligence company focused on real estate valuation and location analyses.
• Görkem Öğüt, Co-Founder and CEO of Endeksa, has been appointed CEO of the new combined organisation.

According to the press release, the merger creates a more transparent, integrated structure across the entire real estate journey. Endeksa’s AI-driven valuation and market analysis will be folded into Emlakjet’s marketplace and professional network. As Görkem Öğüt said: “Our goal is clear: to combine Endeksa’s analytical intelligence with Emlakjet’s platform scale, strengthening a data-driven decision-making culture in the sector. We aim to make property search, valuation, and decision-making processes more predictable and more reliable.”

Arda Ayvaz, Strategy Director at iLab, framed the move as combining “data and marketplace power” inside a single structure and signalled that iLab will introduce “new products and services that deliver the most advanced and comprehensive property search experience in Turkey.” iLab already owns brands such as Kariyer.net, Sigortam.net, Arabam.com, Cimri.com, HangiKredi, Neredekal.com, ChemOrbis, and SteelOrbis — this merger extends the group’s reach into property tech.

Why this matters: strategic rationale and market logic

There are three straightforward strategic logics behind the merger:

• Scale meets intelligence: Emlakjet brings a large user base and nationwide reach, Endeksa offers AI models trained on millions of data points. Combining them creates a single data loop where listing behaviour, transaction outcomes, and valuation models can inform each other.
• Improved user propositions: integrating automated valuation outputs directly into listing pages and agent tools could shorten search-to-purchase timelines and reduce friction for digital-first buyers.
• Platform monetisation: richer data enables new premium products (advanced analytics for investors, lead generation for agents, mortgage-linked valuations for lenders).

From an investor’s point of view, that’s attractive: better data can reduce information asymmetry, improve underwriting for residential and buy-to-let deals, and allow faster market monitoring. From a seller’s or agent’s point of view, the same data can support pricing confidence and marketing decisions.

However, scale and AI integration do not guarantee improved market outcomes. Real estate is local, and automated models often struggle where transaction volumes are thin or where property heterogeneity is high. Emlakjet’s presence in all 81 provinces is a strength for coverage, but the quality of underlying transactional data matters for Endeksa’s models to be reliable across every locale.

How integration could change property search, valuation and investment decisions

Emlakjet-Endeksa promises to alter several stages of the real estate workflow:

• Search and discovery: buyers may see AI-derived price ranges and risk scores as they browse listings. That changes bargaining dynamics — buyers could be more aggressive if valuations show margin over ask prices, while sellers might price to model outputs.

• Valuation and comparables: Endeksa’s automated valuation model (AVM) can generate instant estimates and location analytics. When AVMs are visible on marketplace listings, comparables (comps) will be anchored more to algorithmic outputs than to agent judgement alone.

• Investment screening: investors can use predictive analytics to evaluate rental yield potential, expected price appreciation, and micro-location trends more quickly. That reduces time-to-decision for portfolios and allows scenario testing.

• Transaction transparency: if combined data are made accessible, buyers and lenders can get faster pre-approval and valuation checks. That could speed up deals and reduce falls-through.

Remember, though, that predictive models are only as good as their inputs. Endeksa “analyses millions of data points from all sources” to create valuations. The merger increases the quantity of listing and user-behaviour data available to those models, but qualitative data — recent renovations, title issues, building condition — still require human verification.

Practical implications for buyers, sellers and investors

We outline what each market participant should expect and do.

Buyers

• Treat integrated valuations as strong signals, not certainties. Use them to shortlist properties and set negotiation ranges, but verify with on-site inspections and independent appraisals where needed.
• Expect faster initial affordability checks. If platforms integrate mortgage pre-qualification using valuation outputs, early-stage filtering becomes simpler.

Sellers and agents

• Pricing discipline may increase. When algorithmic valuations are public, poorly priced listings stand out and may attract fewer qualified viewers.
• Agents should embrace data literacy. Those who combine local market knowledge with AVM outputs will have an advantage; agents who ignore algorithmic signals risk losing credibility with data-savvy buyers.

Investors

• Use the merger to improve portfolio monitoring. Predictive location analytics can help identify under-the-radar neighbourhoods, but always cross-check with rental demand metrics and local economic indicators.
• Be cautious about liquidity assumptions. Better data does not create buyers overnight — it improves signal clarity but not necessarily market depth.

Lenders and insurers

• Expect richer inputs for automated risk scoring. Endeksa’s models can feed into credit risk and pricing decisions, potentially lowering underwriting costs.
• Regulators may scrutinise automated valuation usage more closely, particularly where valuation outputs influence lending decisions.

Risks, limitations and governance questions

The promise is large, but the risks are real.