Real Estate Data

Client Background: A Series B PropTech startup based in Austin, Texas, offering AI-powered residential property valuation (AVM) tools for mortgage lenders and real estate agents across 12 US states.

Challenge: The client’s existing AVM model was trained on limited MLS data aggregated manually, resulting in 15–20% valuation errors in secondary markets. Their internal data team was spending 60% of engineering hours maintaining brittle scrapers that broke every time Zillow, Redfin, or Realtor.com updated their front-end layouts. Data freshness lagged 48–72 hours — a critical problem in competitive markets with multiple-offer environments.

Solution: Hir Infotech deployed a fully managed AI-guided real estate data pipeline covering Zillow, Redfin, Realtor.com, county assessor records, and 18 state-level MLS feeds. LLM-powered extraction mapped new property fields automatically, eliminating layout-change breakages. Data was delivered via API to the client’s AWS-based ML infrastructure in near real-time, with full schema normalization and deduplication baked in.

Results: AVM model accuracy improved by 34% within 90 days. Engineering hours spent on data maintenance dropped by 73%. The client expanded AVM coverage from 12 to 28 US states within six months, directly driving a 2.4x increase in enterprise lender contracts. Data freshness improved from 48-hour lag to under 90 minutes for priority markets.

Client Testimonial: “Hir Infotech didn’t just solve our scraping problem — they became our data infrastructure. We finally have the coverage and freshness our models need to compete with the big players.” — Head of Data Engineering, Austin PropTech Platform

Client Background: A Luxembourg-based real estate investment fund managing a €2.3 billion diversified portfolio across Germany, Netherlands, France, Spain, and the UK, targeting residential and commercial assets.

Challenge: The fund’s investment committee was making allocation decisions with data that was 4–8 weeks stale, sourced from inconsistent reports by local brokers in each market. There was no unified view of listing price trends, days-on-market, vacancy rates, or transaction volumes across their five target markets — creating blind spots that cost the fund two high-yield commercial acquisitions in Amsterdam and Frankfurt to faster-moving competitors.

Solution: Hir Infotech built a unified real estate data intelligence platform aggregating data from ImmobilienScout24 (Germany), Funda (Netherlands), SeLoger (France), Idealista (Spain), and Rightmove/HM Land Registry (UK). All pipelines were architected with GDPR Article 6 compliance, with lawful basis documentation, data minimization, and full audit trails. Data was normalized into a single schema and delivered to the fund’s Snowflake instance via daily automated feeds.

Results: The fund achieved a unified cross-market property intelligence dashboard refreshed daily for the first time in its history. Response time to emerging market opportunities dropped from 3–4 weeks to 48 hours. In the first year post-deployment, the fund identified and closed three off-market commercial deals in Germany and Spain that were spotted via early price-trend signals — generating an estimated 18% higher yield compared to on-market acquisitions.

Client Testimonial: “We competed with far larger funds and won deals because we saw trends they didn’t. That’s the Hir Infotech edge.” — Chief Investment Officer, Luxembourg Real Estate Fund

Client Background: An ASX-listed REIT with a AUD 1.1 billion residential rental portfolio spanning Sydney, Melbourne, Brisbane, Perth, Adelaide, Canberra, Darwin, and Hobart — managing 4,200+ rental properties.

Challenge: The REIT’s asset management team was manually reviewing rental listing data from Domain.com.au and realestate.com.au on a weekly basis, with no automated benchmarking of rental yield trends, vacancy rates, or competitor pricing. Annual rent review decisions were based on 3–6 month-old data, causing the REIT to systematically underprice rents in high-demand suburbs by an estimated AUD 45–80/week per property.

Solution: Hir Infotech deployed automated rental data scraping pipelines across Domain.com.au, realestate.com.au, Rent.com.au, and suburb-level government vacancy data sources. A custom rental intelligence dashboard was built, delivering suburb-level median rent benchmarks, vacancy rate signals, and competitive listing analysis updated three times per week to the REIT’s asset management team.

Results: In the first full annual rent review cycle, the REIT identified pricing uplift opportunities across 1,840 properties — averaging AUD 62/week per property — generating an estimated AUD 5.9 million in additional annualized rental revenue. Manual data collection time dropped from 18 hours per week to zero. Portfolio-level vacancy rate improved 1.2 percentage points as the team used supply-demand signals to optimize lease renewal timing.

Client Testimonial: “The numbers speak for themselves. Hir Infotech’s rental data pipeline has become one of the highest-ROI technology investments in our asset management stack.” — Head of Asset Management, ASX-Listed REIT

Client Background: A mid-tier UK mortgage lender originating £800 million in residential mortgages annually, with collateral valuation models that fed directly into underwriting decisions and capital reserve calculations.

Challenge: The lender’s internal AVM models relied on HM Land Registry transaction data, which is published with a 3–4 month lag, creating systemic valuation risk during fast-moving market conditions like those seen in UK property markets from 2023 to 2025. Regulatory stress tests flagged this data latency as a material risk in two consecutive audits.

Solution: Hir Infotech layered a real-time property listing intelligence feed on top of the lender’s existing Land Registry data — extracting live asking price data, days-on-market signals, and listing-to-sold ratios from Rightmove and Zoopla at the postcode level. A custom price trend API delivered weekly postcode-level property price indices with <24-hour latency, bridging the 3–4 month gap in official transaction data.

Results: AVM model accuracy at origination improved by 28% as measured against subsequent sold prices. The lender passed its next regulatory stress test with no collateral valuation risk flags. Underwriting decisions became more defensible, contributing to a 12% reduction in mortgage arrears rate in the first 18 months. The compliance and risk team cited the Hir Infotech data layer as a key contributor to improved capital adequacy modelling.

Client Testimonial: “In a regulated industry, data latency is risk. Hir Infotech closed that gap for us — and the regulators noticed.” — Chief Risk Officer, UK Mortgage Lender

Client Background: A Berlin-based PropTech startup building an AI-powered commercial property valuation engine for German institutional investors, private equity firms, and commercial real estate brokers.

Challenge: Commercial real estate data in Germany is notoriously fragmented — spread across ImmobilienScout24, Immowelt, Engel & Völkers listings, local broker databases, and Grundbuch registry records. Building a reliable, continuously updated commercial AVM required aggregating data from 20+ sources in a normalized format — a challenge the startup’s four-person engineering team could not solve without distracting entirely from product development.

Solution: Hir Infotech took full ownership of the data acquisition layer — designing, deploying, and maintaining scraping pipelines across all 20+ German commercial property data sources. GDPR-compliant pipelines were built with legitimate interest documentation for each source category. Structured data was delivered weekly via Google BigQuery integration, including commercial listing price, property class, lease terms, yield indicators, and geographic metadata.

Results: The startup launched its commercial AVM product to market six months ahead of the original roadmap — directly attributed to outsourcing data infrastructure to Hir Infotech. The engine achieved 91.4% valuation accuracy within ±10% of eventual transaction price in back-testing. The company secured €4.2 million in Series A funding, with investors specifically citing data coverage and quality as a key investment thesis driver.

Client Testimonial: “Hir Infotech is the reason we shipped on time and the reason our investors believe in our data moat.” — Co-Founder & CTO, Berlin PropTech Startup

Client Background: A New York-based real estate private equity firm managing USD 1.8 billion in US multifamily and mixed-use assets, with an acquisition team actively sourcing 50–80 potential deals per quarter across Sun Belt and Midwest markets.

Challenge: The firm’s deal sourcing process relied heavily on broker relationships and on-market listings — missing a significant off-market opportunity pool. The acquisition team estimated they were seeing only 30–40% of relevant deals in their target markets (Atlanta, Dallas, Phoenix, Columbus, Indianapolis) due to lack of systematic data-driven sourcing.

Solution: Hir Infotech designed a custom off-market property intelligence pipeline aggregating data from county assessor ownership records, building permit databases, LLC filing registrations, distressed property signals, and pre-foreclosure notices across all five target markets. AI-driven pattern matching identified properties meeting the firm’s acquisition criteria — specific unit counts, age, location, owner profile — before they were listed on-market, delivering a shortlist of 200–400 properties per month to the acquisition team.

Results: The firm’s acquisition pipeline expanded by 2.8x in qualified deal volume within four months. Off-market deal sourcing increased from near-zero to 34% of closed acquisitions in the following 12 months. Off-market deals closed at an average 7.2% discount to comparable on-market transactions — translating to USD 31 million in estimated acquisition cost savings across 8 closed deals in year one.

Client Testimonial: “Our competitors are bidding on the same on-market deals and wondering why returns are compressing. We moved upstream. Hir Infotech made that possible.” — Managing Director – Acquisitions, New York PE Firm

Client Background: A Barcelona-based residential developer launching a 280-unit mixed residential development in three phases across 2024–2026, targeting mid-market and premium buyers in the Greater Barcelona and Valencia metropolitan areas.

Challenge: The developer’s sales and marketing team was setting launch prices based on broker feedback and published market reports — both inherently backward-looking. The risk: over-pricing led to slow absorption and carrying cost bleed; under-pricing left revenue on the table. For a 280-unit project, a 3% pricing error on average meant €4–6 million in value impact either way.

Solution: Hir Infotech deployed a continuous Idealista, Fotocasa, and Habitaclia scraping pipeline covering all competing residential launches within a 10-km radius of each development site — monitoring listing price, price-per-sqm, time-on-market, discount-to-list ratio, and unit mix data. A custom competitive pricing intelligence report was delivered bi-weekly to the sales director and pricing committee, enabling dynamic phase-by-phase launch price calibration.

Results: Phase 1 (84 units) launched at data-informed prices and achieved 91% sold in 60 days — 23% faster than the developer’s historical average absorption rate. Phase 2 pricing was adjusted upward by 4.7% based on competitor sell-through signals detected in the data, generating €1.9 million in additional revenue versus the original plan. The developer has since embedded Hir Infotech’s data intelligence service as a permanent input to all future project pricing workflows.

Client Testimonial: “We stopped guessing and started pricing on evidence. The ROI on this data service was evident within the first sales week.” — Sales & Marketing Director, Barcelona Real Estate Developer