Roundup: SearchMyCampus: Education Worldwide, PerSquareFeet’s Property Price Index

April 23, 2009
By sinha

News roundup:

SearchMyCampus has announced a tie up with Education Worldwide India. The classifieds portal will participate in all major fairs organized by the event organizer during the year. It will promote these fairs online among its database of users. – more details

Persquarefeet (read review), real estate portal has launched property price index  (in a primitive stage)– they collect key data from the thousands of listings above (such as city, area, price, sq.ft. area), and analyze and arrive at an average price per sq.ft. for each area in key cities.

The real estate portal has integrated Oodle API to fetch classified listings on our website. Since most big property portals are sharing their listings with oodle, users can now search property listing from various portals all in one place.

See our recent review of a competing product,100nests.

More Updates:

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               About the author - Ashish Sinha is a Startup Mentor/Product Strategy Coach, and the founder/chief editor of pluGGd.in. He has launched/managed couple of products (consumer as well as enterprise) in US and India, and now consults with startups/small businesses on their product/media strategy. He can be reached at: ashish (at) pluGGd.in [+91 98452 06443]

2 Responses to “ Roundup: SearchMyCampus: Education Worldwide, PerSquareFeet’s Property Price Index ”

  1. PerSquareFeet on April 23, 2009 at 10:06 pm

    Hi Ashish,

    Thanks for talking about the index here. As I had mentioned, we noticed that the classified data is full of flaws, especially plagued by outliers. Even though we are designing our algorithm to deal with outliers and smoothen the curves, we would be glad to get views from the readers of pluggd.in about handling this issue.

    http://www.persquarefeet.in

  2. PerSquareFeet on April 24, 2009 at 9:12 am

    Thanks for covering the property index here. As I told you, the classifieds data is full of incorrect entries which negatively affects the index. Even though we are working on our own algorithm to deal with the outliers in the data, I will be glad if I can get some response from the readers her on how best to tacle this issue.

    http://www.persquarefeet.in

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