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GeoIp2\Model\City Object ( [raw:protected] => Array ( [city] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [continent] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [subdivisions] => Array ( [0] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.150 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.150 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
Enhance property search experiences using the HouseSigma Property Dataset. Learn how one platform improved buyer confidence, and boosted lead conversions by 42%
Note: You’ll receive it via email shortly after submitting the form.
In today’s increasingly competitive real estate environment, homebuyers expect more than just property listings—they demand contextual insights, historical pricing trends, comparables, neighborhood intelligence, and predictive scoring. Traditional listing portals rarely provide this depth, resulting in decision fatigue and buyer uncertainty. By integrating the HouseSigma property dataset, one real estate intelligence platform unlocked powerful market transparency, enabling users to explore pricing patterns, property valuations, and market forecasts in real time. This enriched ecosystem helped reduce analysis paralysis, boosted trust, and encouraged buyers to engage with listings more confidently. As the platform evolved into a data-driven advisory engine, homebuyers transitioned from passive scroll-through behavior to informed engagement, ultimately increasing lead conversions by a remarkable 42%. This case study reveals the steps behind this transformation and how data-driven decision-making redefined user confidence.
The client is a forward-thinking real estate technology company positioned at the intersection of property search, market intelligence, and buyer enablement. Operating across major metropolitan regions, the platform serves first-time buyers, investors, and relocation-driven users who rely on reliable analytics for competitive real estate decisions. Initially, the platform’s core premise offered property discovery and listing exploration, but lacked intelligence layers that modern buyers expect. Integrating the HouseSigma property listings dataset allowed the client to evolve from a simple listing portal into a comprehensive decision-support engine. By incorporating automated insights around valuations, comparables, property appreciation forecasts, and community intelligence, the platform gained an edge over generic search-based solutions. Within months, the client recognized that data depth—not listing volume—was the true catalyst for increased engagement, conversion velocity, and buyer trust.
These issues collectively hindered the platform’s ability to become a destination of trust. The lack of intelligence meant users treated the site as a browsing tool rather than a purchase-enablement platform.
The overarching mission was to transform data into a behavioral catalyst—turning curiosity into action.
Actowiz Solutions began by establishing a future-proof data infrastructure capable of ingesting large-scale property feeds without degrading platform performance. Our engineers created mapping standards, unified schemas, and cleaned historical inconsistencies, ensuring uniformity across critical data elements like price, bedrooms, geographic tags, school zones, and amenities. The backbone of the transformation relied heavily on HouseSigma property data scraping, which supplied real-time metrics such as comparable sales, pricing fluctuations, tax valuations, and projected appreciation scores. Through modular data services, the client gained the ability to plug in incremental datasets without disruption—creating a system that evolved as markets evolved.
Data alone isn’t valuable unless users absorb it effectively. The next phase refined user journeys by introducing contextual intelligence directly onto property listing pages. Interactive charts visualized price trends, comparison tabs showed similar homes, and valuation alerts helped users time offers intelligently. Affordability calculators assessed mortgage feasibility instantly. This blend of interface enhancements and predictive logic reshaped how buyers processed information. Users began spending 2–3x longer on listings, signaling deeper engagement and validated decision paths.
Real estate listings vary by geography, agency, and source APIs. Address formatting, missing tax records, or variant field structures disrupted data continuity. Actowiz implemented dynamic format validators, schema aligners, and fallback enrichment layers to normalize fragmented details across records.
The platform encountered diverse formats—condos, detached homes, duplexes, and investment opportunities—each requiring different intelligence parameters. Our adaptive crawlers empowered Extract Property Listings Data From HouseSigma pipelines to differentiate property classes, enrich metadata, and return structured outputs fit for analytics.
Competitive real estate markets fluctuate daily. Prices shift, open houses appear, and offers close quickly. Our refresh model leveraged event-based triggers connected to listing changes, ensuring daily, hourly, or instant updates based on demand tiers.
We created a unified system powered by Real Estate Property Data Scraping that transformed raw data streams into layered intelligence. Listings gained valuation scores, location-based desirability indicators, property comparables, and appreciation projections—making complex deal evaluation effortless. Automated insights reduced research time from days to minutes. Contextual overlays like crime rate trends, school ratings, commute distances, and local price elasticity allowed buyers to evaluate lifestyle and financial impact simultaneously. These data bundles were delivered through streamlined APIs, enabling seamless scaling across new markets.
The new ecosystem redefined how users interacted with listings. With Real Estate Data Intelligence powering valuations and contextual recommendations, users transitioned from passive browsing to decisive purchasing behaviors.
The platform became a decision engine, not a listing directory. 74% of users reported feeling "more confident" about price fairness, and 61% requested mortgage guidance directly via embedded CTAs—a monetizable revenue channel for the client.
"Actowiz Solutions revolutionized our data foundation. What was once a simple listing website is now a market intelligence powerhouse. Our users rely on us, not just to find homes, but to understand them. The insights unlocked by Actowiz directly contributed to higher conversions, deeper engagement, and improved customer satisfaction. Their expertise and responsiveness made implementation seamless and future-proof."
— Director of Product Strategy, Real Estate Intelligence Platform
Actowiz Solutions delivers scalable, real-time property intelligence using robust data automation frameworks. We specialize in extracting, structuring, and operationalizing massive datasets powered by the HouseSigma property dataset. Our differentiators include:
We don’t just deliver data—we deliver transformation.
The success of this project demonstrates how integrating the HouseSigma property dataset reshaped the client’s platform from a static database into a predictive buying companion. Actowiz Solutions empowered users to explore listings confidently through insights sourced via Web Scraping API, curated Custom Datasets, and accelerated extraction using an instant data scraper. In a market where decisions and timing define value, giving buyers transparent insights yields measurable conversions and enduring trust.
It delivers detailed property insights, including pricing history, tax data, and location trends, helping buyers evaluate homes more accurately.
Through automated pipelines, scheduled refresh cycles, and validation rules that ensure current listings and price changes remain updated.
It eliminates guesswork, increases trust, and provides transparent metrics that simplify property comparisons and speed up decision-making.
Yes. APIs and modular data structures ease integration regardless of tech stack, enabling faster deployment and minimal disruption.
When conducted responsibly using public data and compliance frameworks, property data extraction remains perfectly safe and legally valid.
Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.
Find Insights Use AI to connect data points and uncover market changes. Meanwhile.
Move Forward Predict demand, price shifts, and future opportunities across geographies.
Industry:
Fintech / Digital Payments
Result
Accurate daily voucher &
cashback visibility across platforms
“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”
Product Manager, Fintech Platform (India)
✓ Daily voucher & cashback tracking via Push & Pull APIs
Coffee / Beverage / D2C
2x Faster
Smarter product targeting
“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”
Operations Manager, Beanly Coffee
✓ Competitive insights from multiple platforms
Real Estate
Real-time RERA insights for 20+ states
“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”
Data Analyst, Aditya Birla Group
✓ Boosted data acquisition speed by 3×
Organic Grocery / FMCG
Improved
competitive benchmarking
“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”
Product Manager, 24Mantra Organic
✓ Real-time SKU-level tracking
Quick Commerce
Inventory Decisions
“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”
Aarav Shah, Senior Data Analyst, Mensa Brands
✓ 28% product availability accuracy
✓ Reduced OOS by 34% in 3 weeks
3x Faster
improvement in operational efficiency
“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”
Business Development Lead,Organic Tattva
✓ Weekly competitor pricing feeds
Beverage / D2C
Faster
Trend Detection
“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”
Marketing Director, Sleepyowl Coffee
Boosted marketing responsiveness
Enhanced
stock tracking across SKUs
“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”
Growth Analyst, TheBakersDozen.in
✓ Improved rank visibility of top products
Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.
✔ Scraped Data: Price Insights Top-selling SKUs
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place
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Actowiz Solutions tracks hyperlocal Glovo prices in Barcelona using high-frequency q-commerce scraping to monitor pricing, promos, and availability.
Discover 10 powerful ways data scraping boosts business growth, from competitive price intelligence and demand forecasting to inventory tracking and market monitoring.
UAE E-Commerce & Quick Commerce SKU Data Analysis delivers insights on pricing, availability, trends, and performance to optimize catalogs and growth.
Scraping spices product data from ecommerce helps track prices, availability, brands, and demand trends for smarter sourcing decisions.
Learn how Web Scraping Instacart Product Availability by Zip Code helps retailers track stock, optimize inventory, and improve delivery efficiency
Grab Rewards Data Scraping helps analyze reward points, offers, redemption trends, and user incentives to optimize loyalty and engagement strategies.
Web Scraping Grab Gift Card Data helps track demand, usage patterns, pricing trends, and consumer behavior across digital platforms.
Real-time grocery price changes across Walmart, Instacart and Target. Track top SKU drops, increases and hourly volatility with Actowiz Solutions.
Enhance deep learning performance with large-scale image scraping. Build diverse, high-quality training datasets to improve AI accuracy, object detection, and model generalization.
City-Wise SKU Demand and Pricing Trends - E-Commerce & Q-Commerce multi-Platforms, insights to compare demand, pricing, and growth patterns across cities
UK Grocery Market Analysis 2026 - Tesco, Asda, Sainsbury’s & Morrisons delivers insights on pricing, market share, competition, and consumer trends shaping retail.
Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.
Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.
Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations