Goal: Create an effective SEO campaign that is able to target searches for homes in specific neighborhoods as well as generate leads by providing the best MLS search options online.
– We created a robust MLS website that shows listings for the Greater Toronto Real Estate Area.
– Provide advanced search options, map search features, ability to search real estate listings based on school rankings, and lastly, provide users options to search neighborhoods that would minimize their commute time to work.
– Create neighborhood pages that focused on showing property listings, and allowed the website to target long tail keywords.
– Use the domain relevance factor to rank for the keyword Toronto Real Estate using the home page.
GTR wanted a logo that embodied the city of Toronto. What better way than to create a small skyline and use a time lapse video as their banner. Using cool tones, nice pictures, and the inspiration from this beautiful city, we upgraded and reorganized Greater Toronto Real Estate. With bland and confusing sites, GTR now stands out from its dull and confusing competitors with an application to boot. They know that buying or selling homes can be stressful so we helped create a site that does it all. Search listings, find out what your house is worth, or learn step-by-step the best ways to buy and sell a home. By signing up, users can be one of the first to view new listings they’ve been wanting to see.
GTR is one of the most intense real estate applications to hit the Canadian market. We built GTR in CodeIgniter Framework.
Phase 1: We integrated the Toronto Real Estate Board MLS using IDX integration. All of the properties are pulled daily and stored, organized, and shown to users interested in searching for real estate in Toronto. We organized all of the MLS information into the different cities of the Greater Toronto Area (GTA). We then broke down the properties into specific communities within each specificity.
Phase 2: Using the information in Phase 1, we created a platform where users can get updates on property sells within specified areas. We call it Neighborhood Watch.
Phase 3: Organizing the properties based on which schools they’re zoned to, we then pulled data from third party sites to organize schools based on their ratings. Ultimately, users can search for properties by the rankings of the schools they’re zoned to!
Phase 4: We used Google Maps API to allow users to search properties that would minimize their commute time to work. Even better, we allowed them to input the address of their work place for both them and their spouse/husband. Based on the address, the application would show a list of neighborhoods that would minimize total family commute time per day, month, and year!
Phase 5: We put all of phase 1 through 4 into an iOS APP!