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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
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                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [postal] => Array
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            [registered_country] => Array
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                    [iso_code] => US
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [subdivisions] => Array
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                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                )

            [traits] => Array
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                    [ip_address] => 216.73.216.24
                    [prefix_len] => 22
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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                )

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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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            [validAttributes:protected] => Array
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                    [0] => confidence
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                    [3] => isoCode
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [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
                        )

                )

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            [validAttributes:protected] => Array
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                    [1] => geonameId
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                    [3] => isoCode
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        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [2] => isInEuropeanUnion
                    [3] => isoCode
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.24
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
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                    [10] => isp
                    [11] => isPublicProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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            [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
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                                )

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                    [validAttributes:protected] => Array
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)
 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
)

Introduction

The Indian real estate landscape is evolving rapidly due to demographic shifts, infrastructure growth, and digital adoption. Actowiz Solutions presents this research report to showcase how MagicBricks data extraction and 99acres data scraping can help uncover actionable trends across the country’s residential and commercial property sectors.

This report leverages vast volumes of structured property listings to conduct detailed property listing data analysis from top portals. Using our advanced real estate data extraction tools, we evaluate pricing dynamics, location-wise demand, builder performance, and inventory patterns. With a growing need for data-backed decision-making, real estate data scraping India is no longer optional—it’s essential.

Actowiz helps clients extract real estate data from MagicBricks and 99acres to build high-impact analytics dashboards. Let’s explore how these insights power smart investment and planning decisions.

The Role of 99acres and MagicBricks in Real Estate Intelligence

of live listings. Combined, they cover over 85% of all online residential inventory in tier-1 and tier-2 cities. With such dominance, Web Scraping Real Estate Data from these platforms reveals authentic and real-time market activity.

Actowiz’s property portal data scraping approach captures dynamic attributes like price fluctuations, listing age, BHK type, square footage, and seller category (agent/owner/builder). This granular approach is vital for tools for analyzing Indian property listings across metros and emerging regions.

Average Price Per Sq. Ft. in Metro Cities (2020-2025)

Year Mumbai Delhi NCR Bengaluru Chennai Hyderabad
2020 ₹7,900 ₹6,200 ₹5,500 ₹4,800 ₹4,200
2021 ₹8,100 ₹6,500 ₹5,700 ₹4,950 ₹4,400
2022 ₹8,400 ₹6,900 ₹6,050 ₹5,150 ₹4,750
2023 ₹8,700 ₹7,200 ₹6,300 ₹5,400 ₹5,100
2024 ₹8,950 ₹7,400 ₹6,550 ₹5,650 ₹5,400
2025 ₹9,200 ₹7,600 ₹6,850 ₹5,900 ₹5,750

Analysis: Property prices across metro cities show a steady rise of 15-20% over five years, with Bengaluru and Hyderabad leading growth trends.

Table 2: MagicBricks vs. 99acres - Listing Volume Comparison (2020-2025)

Kroger-Competitors-in-the-US
Year MagicBricks 99acres
2020 1.2 million 1.0 million
2021 1.4 million 1.2 million
2022 1.6 million 1.3 million
2023 1.8 million 1.5 million
2024 2.1 million 1.7 million
2025 2.4 million 1.9 million

Analysis: MagicBricks consistently leads in listing volume, making MagicBricks data extraction essential for holistic market coverage.

Most Searched Property Types (2025)

Kroger-Competitors-in-the-US
Property Type Search Share (%)
2 BHK 38%
3 BHK 29%
1 BHK 21%
Villa 8%
Studio 4%

Analysis: 2 BHK remains the most preferred unit size across India, driven by nuclear families and affordability.

Top 5 Cities by Demand Index (2025)

Kroger-Competitors-in-the-US
City Demand Index
Bengaluru 98
Hyderabad 96
Pune 93
Mumbai 89
Chennai 85

Analysis: South Indian cities continue dominating demand due to IT hubs and infrastructure growth.

Rental Yield (%) - Metro Cities (2020-2025)

Kroger-Competitors-in-the-US
Year Mumbai Delhi Bengaluru Chennai Hyderabad
2020 2.5 2.7 3.2 2.8 3.1
2025 2.8 2.9 3.6 3.1 3.4

Analysis: MagicBricks datasets reveal Bengaluru offers the highest rental yield, improving steadily across all metros.

Builder Listings Growth Rate (2020-2025)

Kroger-Competitors-in-the-US
Year Growth (%)
2020 -
2021 8%
2022 11%
2023 14%
2024 16%
2025 19%

Analysis: Builder participation in online portals is increasing, signifying digital maturity in the sector.

Time on Market for Unsold Listings (Days)

Kroger-Competitors-in-the-US
Platform 2020 2025
MagicBricks 110 75
99acres 115 80

Analysis: Reduced listing time indicates increased platform efficiency and faster transaction cycles.

Tier-2 City Price Growth (2020-2025)

Kroger-Competitors-in-the-US
City 2020 Price 2025 Price Growth (%)
Indore ₹3,500 ₹5,200 48%
Surat ₹3,700 ₹5,100 38%
Nagpur ₹3,200 ₹4,850 51%
Kochi ₹3,000 ₹4,400 46%
Ludhiana ₹3,400 ₹4,800 41%

Analysis: High-growth potential lies in tier-2 markets, often overlooked without 99acres data scraping.

Price Trend Accuracy via Scraped Data (Platform-wise)

Kroger-Competitors-in-the-US
Platform Accuracy %
MagicBricks 92%
99acres 89%

Analysis: Scraped data from MagicBricks provides higher listing accuracy, supporting investment decisions.

Average Days to Sell - Builder vs. Owner Listings

Kroger-Competitors-in-the-US
Type 2020 2025
Builder 130 95
Owner 145 110

Analysis: Builder listings now sell 15% faster than individual listings, thanks to better pricing and amenities.

Conclusion

This report highlights the growing importance of structured real estate intelligence using MagicBricks data extraction and 99acres data scraping. With the help of Actowiz Solutions' powerful scraping framework, businesses and investors gain access to real-time, accurate, and city-wise trends across India. Our advanced scraping stack supports Extract MagicBricks Property Data, Web Scraping 99acres Data, and complete 99acres Real Estate Data Scraping pipelines tailored to specific use cases.

From rental yield forecasts to builder performance, Actowiz’s tools provide strategic depth and operational clarity. Real estate professionals, researchers, and data platforms benefit from the ability to unlock real estate insights from MagicBricks and beyond.

Ready to transform your property research with smart insights? Partner with Actowiz for real-time, scalable, and ethical real estate scraping solutions today!

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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:

Coffee / Beverage / D2C

Result

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

Industry:

Real Estate

Result

2x Faster

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×

Industry:

Organic Grocery / FMCG

Result

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

Industry:

Quick Commerce

Result

2x Faster

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

Industry:

Quick Commerce

Result

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

Industry:

Beverage / D2C

Result

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

Industry:

Quick Commerce

Result

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

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

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

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All
Blog
Case Studies
Infographics
Report
Oct 28, 2025

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Web Scraping Whole Foods Promotions and Discounts Data to Optimize Grocery Pricing Strategies

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Scrape USA E-Commerce Platforms for Inventory Monitoring - Tracking 5-Year Stock Trends Across 50,000+ Online SKUs (2020–2025)

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Oct 28, 2025

Scraping Consumer Preferences on Dan Murphy’s Australia - Unveiling 5-Year Trends Across 50,000+ Alcohol Listings (2020–2025)

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Oct 27, 2025

Scraping APIs for Grocery Store Price Matching - Comparing Walmart, Kroger, Aldi & Target Prices Across 10,000+ Products

Discover how Scraping APIs for Grocery Store Price Matching helps track and compare prices across Walmart, Kroger, Aldi, and Target for 10,000+ products efficiently.

Oct 26, 2025

How to Scrape The Whisky Exchange UK Discount Data to Track 95% of Real-Time Whiskey Deals Efficiently?

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Web Scraping Whole Foods Promotions and Discounts Data to Optimize Grocery Pricing Strategies

Discover how Web Scraping Whole Foods Promotions and Discounts Data helps retailers optimize pricing strategies and gain competitive insights in grocery markets.

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AI-Powered Real Estate Data Extraction from NoBroker to Track Property Trends and Market Dynamics

Discover how AI-Powered Real Estate Data Extraction from NoBroker tracks property trends, pricing, and market dynamics for data-driven investment decisions.

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How Automated Data Extraction from Sainsbury’s for Stock Monitoring Improved Product Availability & Supply Chain Efficiency

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Scrape USA E-Commerce Platforms for Inventory Monitoring - Tracking 5-Year Stock Trends Across 50,000+ Online SKUs (2020–2025)

Scrape USA E-Commerce Platforms for Inventory Monitoring to uncover 5-year stock trends, product availability, and supply chain efficiency insights.

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Maximizing Margins - Scraping Online Liquor Stores for Competitor Price Intelligence to Monitor Competitor Pricing in the Online Liquor Market

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Real-Time Price Monitoring and Trend Analysis of Amazon and Walmart Using Web Scraping Techniques

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