With real estate markets digitalizing at a remarkable pace, there’s a growing — and largely unmet — need for machine learning systems that can harness multi-institutional data without putting privacy or regulatory standing at risk. In this paper, we present a Privacy-Preserving Federated Learning (PP-FL) framework built specifically for digital real estate ecosystems. Our approach lets distributed stakeholders — property agencies, government land registries, financial institutions, and PropTech platforms — collaboratively train predictive models without ever pooling their raw transaction or personal records in one place. We’ve designed the system around three interlocking privacy layers: a DP-SGD-based differential privacy optimizer, a homomorphic encryption scheme for gradient transmission, and a secure multi-party computation protocol to safeguard intermediate model states. On top of that, a blockchain-backed audit mechanism using zero-knowledge proofs provides verifiable, regulator-friendly compliance. When we tested the framework on a simulated dataset of 2.4 million real estate transactions spanning multiple institutional clients, it achieved 91.8% prediction accuracy — just 2.4 percentage points behind a fully centralized model — while holding the differential privacy budget to ε = 0.5, cutting communication overhead by 65% relative to naive federated approaches, and satisfying both GDPR and RERA requirements. We believe these results make a strong case that high-utility, privacy-first collaborative learning is not just theoretically possible but practically deployable in today’s real estate sector.

We set out to answer a practical question: can competing real estate institutions train useful shared models without sacrificing the data sovereignty, regulatory compliance, and competitive confidentiality they all depend on? Our PP-FL framework suggests the answer is yes. By combining differential privacy, holomorphic encryption, secure multi-party computation, and block chain-based auditing, we’ve shown it’s possible to reach 91.8% prediction accuracy on a complex real estate valuation task while holding the privacy budget to ε = 0.5 — a combination we believe represents a genuinely viable operating point for real-world deployment. We think this work makes a meaningful contribution to the federated learning literature by moving beyond generic FL setups and addressing the particular constraints of real estate data ecosystems — regulatory, competitive, and institutional. The empirical validation is encouraging, and the theoretical guarantees are solid. That said, we’re well aware of what this study doesn’t cover, and we see several directions worth pursuing. On our agenda going forward: we want to extend the framework to cross-silo settings where clients use heterogeneous model architectures; explore personalized FL variants that preserve global privacy while adapting to regional market conditions; investigate the use of federated knowledge graphs for richer property attribute reasoning; and, most importantly, move beyond simulation to real institutional deployment in Indian and European real estate markets where these privacy tensions are most acute.

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